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d654937051919c9b5fa660ee623243de302a5a0e58e99ed6e66e28abc8566542 | Text | 10,994 | 228 | # Regularized Optimal Mass Transport (rOMT) Applied in Glymphatic-Lymphatic fluid flows
## Introduction
This project includes code for <br />
(1) rOMT algorithm <br />
(2) Lagrangian representation of Glymphatic Dynamics (GLaD) analysis <br />
where (1) runs the main rOMT model on the dataset and (2) post-processes ... |
379240803a8a1ad78e8135db06845e60311a14b54cc1b52323b8043d4cbc1526 | Text | 11,150 | 201 | ---
output: github_document
---
# Purpose
This repository contains all the functions to perform Tilted-CCA (version `1.0.0.001` as of October 4, 2022) and the downstream analysis, for the paper "Quantifying common and distinct information in single-cell multimodal data with Tilted-CCA". See the companion GitHub packa... |
dcc8226df2fb5989daae122cc1f4ec0da1eae7bbb4522569671bb4f94db6adf7 | Text | 11,223 | 256 | # BCBlib
[](https://badge.fury.io/py/bcblib)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses... |
fac8462543d87780163fa0f0508932e1ede73b848c55adbc14b2305199c2a81f | Text | 11,314 | 168 | # SingleCellFusion
SingleCellFusion is a computational tool to integrate single-cell transcriptome and epigenome datasets. Code in this repository is used in [Luo et al., (2019) *BioRxiv*](https://www.biorxiv.org/content/10.1101/2019.12.11.873398v1) and in [Yao et al., (2020) *BioRxiv*](https://www.biorxiv.org/content... |
0cc3b1ce8388d395acdec5a167cd225367303a72b1868b7c0901a9ddc0de6164 | Text | 11,441 | 231 | # Cracking Double-Blind Review: Authorship Attribution with Deep Learning
This is the code for the paper [**Cracking Double-Blind Review: Authorship Attribution with Deep Learning**](https://r... |
c562cdd8ad206f8ce49ab05e7aa750b565e0be24951d30cced9855bb1e1d6a45 | Text | 11,491 | 227 | 
[](https://badge.fury.io/py/flair)
[](https://doi.org/10.5281/zenodo.15735111)
MutFormer is an application of the BERT (Bidirectional Encoder Representations from Transformers) NLP (Natural Language Processing) model with an added adaptive vocabulary to protein context, for the... |
bff8e940ddcc8e422465f3abeb050c30b0c513d3cdc90b49dad27419b64f1399 | Text | 11,740 | 206 | # THIS REPOSITORY IS NOW DEPRECATED
Use [JaneliaSciComp/VVDViewer](https://github.com/JaneliaSciComp/VVDViewer) instead.
# VVDViewer
[](https://doi.org/10.5281/zenodo.5841616)
This is the open-source repository for VVDViewer, an interactive rendering t... |
28cc4f79549d13b2e6b4e9e89bcf8c1c1ae83f5805729476801173555ce71d6f | Text | 12,039 | 190 | # OpenScope Credit Assignment Project Stimulus Code
This repository contains the code needed to reproduce the stimuli used in the **Credit Assignment project**, an [Allen Institute for Brain Science](https://alleninstitute.org/what-we-do/brain-science/) [OpenScope](https://alleninstitute.org/what-we-do/brain-science/r... |
4ed5d1099da6b439310d5a725e5bf38692ce0cd8a363c135701220aced4f1caf | Text | 12,052 | 188 |
# Cufflinks
The main *website* for cufflinks is [here](http://cole-trapnell-lab.github.io/cufflinks/)
*NOTE*: If you're looking for old releases of Cufflinks, including source, you can find them [here](http://cole-trapnell-lab.github.io/cufflinks/install/).
Cufflinks assembles transcripts, estimates their abundance... |
0ef733d3be6c69cb5ddab7b17752906c8f74aae652fd770122e80c64f235251a | Text | 12,168 | 190 | # PiXi
PiXi is an R implementation of Piya, DeGiorgio, and Assis' (2022) three machine learning algorithms for predicting expression divergence between single-copy orthologs and their expression optima in two species.
-----------
Citing PiXi
-----------
Thank you for using PiXi.
If you use this R package, then pleas... |
75ad3e0de21c5cade254143d329195cdeee8bdb5942bc7c66eedbb696f71ad24 | Text | 12,204 | 282 | <div align="center"><img src="https://raw.githubusercontent.com/optuna/optuna/master/docs/image/optuna-logo.png" width="800"/></div>
# Optuna: A hyperparameter optimization framework
[](https://www... |
296c5fde0a2af7c6f12b1ae638b9a87e05a1d4a6ddfdb83f457ec69fcddfeb0c | Text | 12,328 | 148 | ## SUPREME: Multiomics Data Integration Using Graph Convolutional Networks
To learn more about SUPREME, read our paper at: https://www.biorxiv.org/content/10.1101/2022.08.03.502682v1
<img src="https://ziynetnesibe.com/wp-content/uploads/2022/07/SUPREME.png" width="819" height="439"/>
<!-- ![SUPREME pipeline] -->
... |
ba5235c592a7c0e5ed01818033e1dea4420711d04564d5dad916c2bc756c09a2 | Text | 12,624 | 352 | # ExplaiNN
<img src="./data/figs/ExplaiNN.png" height="55%" width="55%" />
`ExplaiNN` is an adaptation of neural additive models ([NAMs](https://arxiv.org/abs/2004.13912)) for genomic tasks, where predictions are computed as a linear combination of multiple independent CNNs. Each CNN consists of a single convolution... |
9552e5569abc8894613b6ee72c6d7d390795f4b549201449c1381e1043eb339b | Text | 12,908 | 218 | [](https://github.com/ANTsX/ANTs/actions/workflows/ci-docker.yml)
[](https://hub.docker.com/repository/docker/antsx/ants)
](https://pypi.org/project/vp-suite/)
[](https://pepy.tech/project/vp-suite)
[ (amine.ghozlane@pasteur.fr) (@xealf8)
## Contents
- [Introduction](#introduction)
- [Process](#process)
- [Installation](#installation)
- [Command line options](#command-line-options)
... |
53a6699de9203deae58befd10c47d6656e6fc7def5e4b6d2addac13f3a261a5a | Text | 13,290 | 286 | # Akkademia
Akkademia is a tool for automatically transliterating Unicode cuneiform glyphs. It is written in python script and uses HMM, MEMM and BiLSTM neural networks to determine appropriate sign-readings and segmentation.
We trained these algorithms on the RINAP corpora (Royal Inscriptions of the Neo-Assyrian Peri... |
8b812d1cf3368abc8abfe04b4df38be693b1b96ef3109bd69ed6d7e7e2dba613 | Text | 13,310 | 358 | # AxialTAD
> An Axial Contrast Architecture for TAD Boundary Prediction from Hi-C
AxialTAD is a deep learning method that predicts topologically associating
domain (TAD) boundaries from Hi-C contact matrices. The model is built around
the Axial Contrast Module (ACM), an architectural component that captures axial
(ro... |
377f9fbc63ae7fdeb624c39eab2e69db5244533c19c0caa77d81e78053df2687 | Text | 13,359 | 91 | # Predictive neural representations of naturalistic dynamic input
DOI code: 10.5281/zenodo.7941212
This repository contains Matlab code accompanying the scientific article available at
De Vries, I.E.J., Wurm, M.F. Predictive neural representations of naturalistic dynamic input. Nat Commun 14, 3858 (2023). https://d... |
4e0526f6ff55e8959e76d469303b4622df06775721129d51972c21b95dcc6d78 | Text | 13,360 | 87 | # Predictive neural representations of naturalistic dynamic input
This repository contains Matlab code accompanying the scientific article, currently available as pre-print, titled: "Predictive neural representations of naturalistic dynamic input", by Ingmar E.J. de Vries and Moritz F. Wurm.
- The article is avail... |
c1b0fad841a91cebbfc98c171cecd81b31566ca11be910a582f633d1095dfa7d | Text | 13,448 | 226 | # README
This is the readme file for getting started with EEG real-time processing using BCI-HIL. The repo includes two demos of real-time BCI applications:
* Motor Imagery
* Clear by Mind
## BCI-HIL: Brain Computer Interface Human-in-the-Loop Research Framework:
The functionality and contents of this framework... |
dacaf2ece078dcbe11d36df838c623e8cdc470ae3d0354d98d3ddbc2951f450b | Text | 13,611 | 214 | [](https://badge.fury.io/py/palantir)
[](https://codecov.io/github/settylab/Palantir)
Palantir
------
Palantir is an algorithm to align cells along differentiation trajectories... |
06b818d249f40c82560364e688d164f51c30dbf198e561786ee58e28da8d74d2 | Text | 14,345 | 406 | # Corset-tools
Companion tools for working with transcript clusters generated by
[Corset](https://github.com/Oshlack/Corset) (Davidson and Oshlack, 2014).
All four helper tools are now subcommands of a single `corset-tools` entry point:
| Subcommand | Purpose ... |
1acc4d9f6399480f17c493fa6fc1b1f1d77d0334ececa24315f593a72631f220 | Text | 14,362 | 391 | ======================================
Hippocampal Segmentation Factory (HSF)
======================================
Exhaustive documentation available at: `hsf.rtfd.io <https://hsf.rtfd.io/>`_
**Current Models version:** 4.0.0
.. list-table::
:header-rows: 1
* - Python Package
- Code Quality
- ... |
b914cff07e45fb989c859317bfb7b972e5084d40f081a87cd114032cc07544ca | Text | 15,190 | 468 | <img src="https://fzhang.bioinfo-lab.com//img/inferloop_logo.jpg" width="250">
**InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop**, ***Briefings in Bioinformatics*, 2023, https://doi.org/10.1093/bib/bbad166**
This tool is designed for inferring loop signals of cell clusters ... |
4840ced4d2ef0f11272549c91eda0172ac06cf547d1855c376ba4f070b558367 | Text | 15,461 | 299 | # MotionSense Dataset
This dataset includes time-series data generated by accelerometer and gyroscope sensors (attitude, gravity, userAcceleration, and rotationRate). It is collected with an iPhone 6s kept in the participant's front pocket using [SensingKit](https://www.sensingkit.org/) which collects information from ... |
b65e18c92e74c4f31b4410dfdb1412e540786ffcd83ae21763478d01610bc4a4 | Text | 15,568 | 269 | # pycma
[](https://circleci.com/gh/CMA-ES/pycma/tree/main)
 priors across populations.
- T... |
2126650a7c5fd5db708ef4da37a37132e4c220f63c90177a1026d894e5fd4457 | Text | 15,625 | 255 | # <img src="https://raw.githubusercontent.com/vejnar/ReadKnead/main/img/logo.svg" alt="ReadKnead" width="45%" />
[](https://mozilla.org/MPL/2.0/)
<p align="center" style="text-align:center;"><i>Knead your sequencing reads before ba... |
9dfb78a706c79450b4b96d6bb209de8196fd0d64d24066b90db3d945dcd6ac59 | Text | 16,486 | 248 |
## Welcome to sktime
<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.svg" width="175" align="right" /></a>
> A unified interface for machine learning with time series
:rocket: **Version 1.2.0 out now!** [Check out the release notes here](https://w... |
f35108fc9b11946aecdfd939ea6450b4743a16ab00ccc15cbefa11d64beecd32 | Text | 16,836 | 430 | # Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
[[arxiv](https://arxiv.org/abs/2203.11444)] [[chemical science]](https://doi.org/10.1039/D2SC02763A)
The directory contains source code of the article:
Zhong et al's Root-aligned SMILES: A ... |
773682c0597389b4d9de5b67cde4768a71895f8de3ef285ed134e98d7e596f7f | Text | 17,140 | 306 | # q2-matchmaker
A software package for case-control differential abundance analysis
I want to stress that this software package is research software and may be challenging to run. Running this software requires a super computing cluster with 100s of cores, in addition to a very involved downstream analysis.
For examp... |
74d75203f0e898f252ff67cf74dfd6635082046a53aec568f60bc28e0c9e649b | Text | 17,316 | 321 | # <img src="https://raw.githubusercontent.com/vejnar/LabxPipe/main/img/logo.svg" alt="LabxPipe" width="45%" />
[](https://mozilla.org/MPL/2.0/)
* Integrated with [LabxDB](https://labxdb.vejnar.org): all required annotations (... |
032a9bfdeeb29f7efd7c58a97eeb52e1354a47041d134fde38f9c0fa043deba9 | Text | 17,370 | 292 | # MOTHe-GUI
<br>
<img height="350" src="https://github.com/tee-lab/MOTHe-GUI/blob/master/gui_screenshots/bb_track.gif">
<br>
<br>
<img height="350" src="https://github.com/tee-lab/MOTHe-GUI/blob/master/gui_screenshots/wasp_track.gif">
<br>
**Mothe is a python-based application with GUI to detect and track multiple a... |
c935370eb308d3bf1f88f7449ad8041d99a346f951127ddf9b70d9b79618092e | Text | 17,515 | 357 | # slivar: filter/annotate variants in VCF/BCF format with simple expressions [](https://github.com/brentp/slivar/actions/workflows/ci.yml)
[](https://anaconda.org/bi... |
fd502ac06f94d18f8549c7ff8f52d0074efdf7923b7f663247bee809a4f8ce67 | Text | 17,523 | 492 | Face Recognition
================
| Recognize and manipulate faces from Python or from the command line
with
| the world's simplest face recognition library.
| Built using `dlib <http://dlib.net/>`__'s state-of-the-art face
recognition
| built with deep learning. The model has an accuracy of 99.38% on the
| `Labe... |
43437210284745a3cb0fd4fcdac9e7bf931421e716049c56baad88397c52bc24 | Text | 17,691 | 505 | # AQDnet
**AQDnet: Deep Neural Network for Protein–Ligand Docking Simulation**
AQDnet is a deep neural network system for **protein–ligand docking and scoring**.
It takes the **3D structure of a protein–ligand complex** as input and predicts a **binding affinity score** that can be used for docking pose evaluation ... |
c2bdfb525ecd6feb535f3d0249c531f6748d2aed236fac9ee82a7e569e777219 | Text | 17,810 | 220 | # <img src="https://raw.githubusercontent.com/vejnar/GeneAbacus/main/img/logo.svg" alt="GeneAbacus" width="85%" />
From high-throughput sequencing **mapped reads** ([SAM/BAM](https://samtools.github.io/hts-specs/)), **GeneAbacus**:
* Creates **profiles** representing coverage depth per nucleotide,
* **Counts** reads m... |
b327af88ba55ea1121c794e158118c55cfc2bb6bdb58f80d4970f53d9e5c2729 | Text | 18,239 | 428 | <img src="images/PACNet_icon.jpg" width=200>
# Platform-Agnostic CellNet
### Introduction <a name="introduction"></a>
Platform-Agnostic CellNet (PACNet) is our newest version of CellNet that is agnostic to transcriptome profiling method, computational preprocessing method, and gene availability arising from preproce... |
5205865a66b8a0c9daccdf8fb14457f16a9d886d4ed781a751451816687a2201 | Text | 19,375 | 322 |
<div id="crazyara-logo" align="center">
<br/>
<img src="./etc/media/crazyara_logo_medium.png" alt="CrazyAra Logo" width="512"/>
<h3>A Deep Learning Chess Variant Engine</h3>
</div>
<div id="badges" align="center">
[ or [in Korean 한국어](https://github.com/ageitgey/face_recognition/blob/master/README_Korean.md) or [in Japanese 日本語](https://github.com/m-i-k-i... |
a5c8fe653e68f57dede6f14496f584ed4ca2e3fb185735ab310063860e81a08c | Text | 20,131 | 228 | # rxnebm
[Improving the performance of models for one-step retrosynthesis through re-ranking](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-022-00594-8)
Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurr... |
ea3724b5e0573e49369666d82584bdafc9df10b0ab5a5cbdf23371011f5a398c | Text | 20,977 | 587 | TrainSpikingNet.jl uses recursive least squares to train fluctation-driven
spiking recurrent neural networks to recapitulate arbitrary temporal
activity patterns. See [Arthur, Kim, Chen, Preibisch, and Darshan
(2023)](https://www.frontiersin.org/articles/10.3389/fninf.2023.1099510/full)
for further details.
# Requir... |
e60a307ed564585079e8f1cc0bd2bbe938bf94e59ae89f5837c8f41c44f70f2b | Text | 21,344 | 497 | # torchinfo
[](https://www.python.org/downloads/release/python-3140/)
[](https://badge.fury.io/py/torchinfo)
[](https://anacon... |
fad0c86b79ff006e54d2ec14ef5006931ad85e290bb3f8b92ccca4b381a4e8b8 | Text | 22,047 | 327 | This repo contains all PDBs generated and metrics calculated to benchmark the predictive modeling performance of AlphaFold2, RoseTTAFold, ESMFold, and OmegaFold on a set of 69 [CASP15](https://predictioncenter.org/casp15/index.cgi) single-chain protein targets with available experimental coordinates.
The predicted str... |
5e8a1c7da618ae105553cf03562984ccf4472866af34dc3fb0e2b4b0a673f03b | Text | 22,383 | 335 | # The transformative power of transformers in protein structure prediction
by Bernard Moussad, Rahmatullah Roche, Debswapna Bhattacharya
published in [PNAS](https://www.pnas.org/doi/abs/10.1073/pnas.2303499120)
<a href="https://zenodo.org/doi/10.5281/zenodo.8153086"><img src="https://zenodo.org/badge/607225167.svg" ... |
616a0893d9ab57077660d63afb4f1aaa67c1c9ba17b4ce41962444b15953a6b8 | Text | 24,434 | 436 | A Note About the Branches
=========================
There are 2 branches in this public repository for CNModel.
The "master" branch is the original branch that goes with Manis and Campagnola, Hear. Res. 2018. This is a Python 2.7 branch.
The "python3" branch is new (June, 2019). It has been tested with Python 3.6 and... |
134e86c584d1e6c62ae4bcf880a808fa126806387ad80b83d443a8829b426060 | Text | 24,716 | 180 | # celltool
Celltool is a collection of tools for analysis of shapes from images, and in particular, for analyzing cell shapes from micrographs. Celltool provides methods for extracting shapes from images, aligning and measuring those shapes, plotting shapes, and statistically comparing distributions (usually of shape ... |
ba6bfa5658b8dc1ce1aaf947f70c5832ec6f912e829ad05034e56ac469998c85 | Text | 27,494 | 654 | Read and write TIFF files
=========================
Tifffile is a Python library to
(1) store numpy arrays in TIFF (Tagged Image File Format) files, and
(2) read image and metadata from TIFF-like files used in bioimaging.
Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, STK, LSM, SGI,
NIHImage, ImageJ, M... |
04f76b8ce74529fc6221278abdb3f8fef1c586c7edf917af91aade5d4c0da976 | Text | 33,585 | 889 | ..
This file is generated by setup.py
Read and write TIFF files
=========================
Tifffile is a comprehensive Python library to
(1) store NumPy arrays in TIFF (Tagged Image File Format) files, and
(2) read image and metadata from TIFF-like files used in bioimaging.
Image and metadata can be read from TIFF... |
5b878942a51e6f8d7adea68dd7189411d1bfa24995d78197d52b2929efb3e091 | Text | 35,702 | 454 | # Convolutional Neural Network Visualizations
This repository contains a number of convolutional neural network visualization techniques implemented in PyTorch.
**Note**: I removed cv2 dependencies and moved the repository towards PIL. A few things might be broken (although I tested all methods), I would appreciate ... |
d6707e198eb00413dd5526aa584f7cc80ea50a0246c5bbdd2a79f4109d4bc519 | Text | 44,898 | 777 | # Introgression detection
## Contact
<https://sites.google.com/view/laurits-skov>
---
## Helpful files
The outgroup files, mutation rate files, reference genomes, ancestral alleles and callability files and ancestral allele files are now premade!
<https://doi.org/10.5281/zenodo.11212339> (hg19 and hg38)
Using th... |
1479d7b26e8b0948a382fd527c4ede4356b981b67066e64bb097d093203bb861 | Text | 55,058 | 516 |
<!-- README.md is generated from README.Rmd. Please edit that file -->
# pubmedR <img src="man/figures/logo.png" align="right" height="139" />
<!-- badges: start -->
[](https://CRAN.R-project.org/package=pubmedR)
[![CRAN
downloads](https://cranlogs.r-pkg.o... |
facb7c063e5a9afec65fd2e3085c79f2c24c4cee6d0614710badb5a2bed83a72 | Text | 200,055 | 1,641 | Awesome Public Datasets
=======================
.. image:: https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg
:alt: Awesome
:target: https://github.com/sindresorhus/awesome
This is a list of `topic-centric public data sources <https://github.com/awesomedata/awes... |
94b37638d7b21ccc2e1ecd1afa9984d2ad80e9baaa2bf713aba473e583052223 | TypeScript | 48 | 1 | import { InjectionToken } from "@angular/core";
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a02687b3cf5031ea5524d3d2a9991ff4a99c62c9391ca937eb8533c416b557b6 | TypeScript | 55 | 1 | export { ComponentsModule } from './components.module'
|
550d922bd6c0d5f23c190caa4274d2340acca89c13ac5de10a9040f02cb69baf | TypeScript | 66 | 3 | export {
AngularMaterialModule
} from './angularMaterial.module' |
4f6f56cfb46a9d9fc5440203a0b4eca3a79100aed9179a7ad6f0ca4379c4cbe9 | TypeScript | 68 | 4 | export type PluginManifest = {
name: string
iframeUrl: string
}
|
1e0597b88cc935cc9d4474d03a33f2d6870ae1328285aaa124964b44eda42d69 | TypeScript | 77 | 4 | declare module '*.md' {
const content: string;
export default content;
}
|
e3721e57ed3da112689ffdb5f66aa47ff9e63cbdaf24c46a81e5403effd89d04 | TypeScript | 107 | 2 | export { DragDropFileModule } from "./module"
export { DragDropFileDirective } from "./dragDrop.directive"
|
0948e7122f26f4eba90c84f0bc1896a05d01b581d0f2d654687170ba10fcc6bb | TypeScript | 114 | 1 | export { TNgAnnotationAABBox, AnnotationLayer, TNgAnnotationPoint, TNgAnnotationLine } from "./annotation.service" |
0ac85ff5a1efe02026b39e5ed68cf5e2c5f26d401f227a44dceac1503f696a18 | TypeScript | 122 | 5 | import { SAPI } from "../sapi.service";
export class SAPIAtlas{
constructor(private sapi: SAPI, public id: string){}
}
|
26f2715119f083517ed2ae7d5b426e8985ed5c43fb6a315469755fcbe96b75c4 | TypeScript | 124 | 2 | export { NehubaUserLayerModule } from "./module"
export { UserLayerDragDropDirective } from "./userlayerDragdrop.directive"
|
61b45f8a6458b32459b336716a3571291a4c2cb0f4e0a874b7d8e6f1f0213f31 | TypeScript | 131 | 5 | import { TRegionSummary } from "src/util/siibraApiConstants/types";
export type TMouseOverVtkLandmark = {
landmarkName: string
} |
5421b2ac73ca23bb218cfd5b5aae442ef623f5e7a8074d42c62d6ef1fb602a95 | TypeScript | 176 | 4 | export { SAPIAtlas } from "./sapiAtlas"
export { SAPISpace } from "./sapiSpace"
export { SAPIParcellation } from "./sapiParcellation"
export { SAPIRegion } from "./sapiRegion"
|
cd968ab29531ffc8fb001cbcc1c35b6f76d6af7693d4cc979820b6ee538a84f0 | TypeScript | 178 | 4 | export { UserPreference, reducer, defaultState } from "./store"
export { nameSpace } from "./const"
export * as actions from "./actions"
export * as selectors from "./selectors"
|
62966b188ee85798b228e34ae521d5dc0df0948e4a98fa9fe36cc3271570e147 | TypeScript | 189 | 3 | export { MouseHoverDirective } from './mouseover.directive'
export { MouseoverModule } from './mouseover.module'
export { TransformOnhoverSegmentPipe } from './transformOnhoverSegment.pipe' |
976055a1cb52fa407474b9874c02426e1e41691d4a5f167eb2c0e49ecb5b8fd2 | TypeScript | 197 | 13 | export { SAPIModule } from './module'
export { SAPI } from "./sapi.service"
export {
SAPIAtlas,
SAPISpace,
SAPIParcellation,
SAPIRegion
} from "./core"
export {
IDS
} from "./constants" |
6e9fc8c62430daf59fc1310baa96a21a42535447b389a77b9ffe227f2f259d46 | TypeScript | 215 | 14 | export {
NehubaConfig,
NgConfig,
NgConfigViewerState,
NgLayerSpec,
NgPrecompMeshSpec,
NgSegLayerSpec,
} from "./type"
export {
getParcNgId,
getNehubaConfig,
defaultNehubaConfig,
} from "./util"
|
c21594dc4b780f068c462541da08793cc2645b96fd6ff760f94a2e40b1c65898 | TypeScript | 215 | 5 | export * as actions from "./action"
export * as selectors from "./selector"
export * as const from "./const"
export { nameSpace } from "./const"
export { reducer, AtlasAppearanceStore, defaultState } from "./store"
|
9af8791a6094f53d23ec48510dec226d780932c4dcb47029e8f2e4e74914c129 | TypeScript | 219 | 12 | import { NgModule } from "@angular/core";
import { SapiViewsCoreModule } from "./core";
@NgModule({
imports: [
SapiViewsCoreModule,
],
exports: [
SapiViewsCoreModule,
]
})
export class SapiViewsModule{} |
660aa619ad0db01b01c498b08b6ef8f629b9a7838a14497813f9d4f09afc20cd | TypeScript | 226 | 8 | import { TransformPdToDsPipe } from './transform-pd-to-ds.pipe';
describe('TransformPdToDsPipe', () => {
it('create an instance', () => {
const pipe = new TransformPdToDsPipe();
expect(pipe).toBeTruthy();
});
});
|
b1b2b9fc3215b24b7bd601fa689b262710146c32a6d323115470d97f52428fed | TypeScript | 234 | 15 | export {
actionRemoveAuxMesh,
actionSetAuxMesh,
actionSetAuxMeshes,
} from './actions'
export {
selectorAuxMeshes
} from './selectors'
export {
reducer
} from './store'
export {
IAuxMesh,
INehubaFeature,
} from './type'
|
e407625980a7c107158b225433e5fc2e00d85be55056eafd972c0c61e8e82e39 | TypeScript | 255 | 12 | import { Pipe, PipeTransform } from "@angular/core";
@Pipe({
name: 'getProperty',
pure: true
})
export class GetPropertyPipe implements PipeTransform{
public transform(input: any, property: any = '@id') {
return input && input[property]
}
}
|
8d9aa80505d1fba82c121b6e2ca2cedda5a0edb931856accc2b84f7df0f77183 | TypeScript | 274 | 17 | import { Pipe, PipeTransform } from "@angular/core";
type TObj = Record<string, any>
@Pipe({
name: 'mergeObj',
pure: true
})
export class MergeObjPipe implements PipeTransform{
public transform(o1: TObj, o2: TObj){
return {
...o1,
...o2
}
}
}
|
6bed39c4a81d2fce67494f47e09e8157fb5f1b1444232a6afbbcffc4c38a47d4 | TypeScript | 307 | 15 | import { SxplrRegion } from "src/atlasComponents/sapi/sxplrTypes";
import { INavObj } from "./constants";
export type TNehubaContextInfo = {
nav: INavObj
mouse: {
real: number[]
voxel: number[]
}
nehuba: {
layerName: string
labelIndices: number[]
regions: SxplrRegion[]
}[]
}
|
ee4cbb1f60152a70da9dec7f8393a1ca560033e8667f838237f8db05c9f80a53 | TypeScript | 307 | 10 | import { CommonModule } from "@angular/common";
import { NgModule } from "@angular/core";
import { DragDropFileDirective } from "./dragDrop.directive";
@NgModule({
imports: [CommonModule],
declarations: [DragDropFileDirective],
exports: [DragDropFileDirective],
})
export class DragDropFileModule {}
|
504ce18359a425884747214870639dc7400ac5d34854d1c7da7e074fb87feee3 | TypeScript | 319 | 13 | import { Pipe, PipeTransform } from "@angular/core";
import { TreeNode } from "./const"
@Pipe({
name: 'flatHierarchySpacer',
pure: true
})
export class FlatHierarchySpacer implements PipeTransform{
public transform<T>(inputNode: TreeNode<T>, ...args: any[]) {
return Array(inputNode.level).fill(null)
}
}
|
524bdc0c86f811b07ca5efe02f71cfb6bd7ca3ee0225dba876f63a2a62132c21 | TypeScript | 340 | 13 | import { Pipe, PipeTransform } from "@angular/core";
import { GroupedFeature } from "./category-acc.directive";
@Pipe({
name: 'grpFeatToName',
pure: true
})
export class GroupFeaturesToName implements PipeTransform{
public transform(groupFeats: GroupedFeature[]): string[] {
return groupFeats.map(f => f.meta... |
7104e3bef4a740898e26dd8cdab4c6c83dae0f21cd1016ca13ea8b13c576959b | TypeScript | 340 | 13 | import { Pipe, PipeTransform } from "@angular/core";
@Pipe({
name: 'reorderPanelIndexPipe',
})
export class ReorderPanelIndexPipe implements PipeTransform {
public transform(panelOrder: string, uncorrectedIndex: number) {
return uncorrectedIndex === null
? null
: panelOrder.indexOf(uncorrectedInde... |
455ab81517272cc4b85281228bbe29eed96481f7f35b12c18dfdfca96a6968c4 | TypeScript | 347 | 16 | import { createAction, props } from "@ngrx/store";
import { MainState, nameSpace } from "./const"
export const generalActionError = createAction(
`${nameSpace} generalActionError`,
props<{
message: string
}>()
)
export const generalApplyState = createAction(
`${nameSpace} generalApplyState`,
props<{
... |
1e26aba79b6a7115fa75d2d9c7187ca687b8d27fab1122a36fffcfe16b03eebc | TypeScript | 353 | 13 | import { Pipe, PipeTransform } from "@angular/core";
import { SxplrParcellation } from "src/atlasComponents/sapi/sxplrTypes";
@Pipe({
name: 'parcellationDoiPipe',
pure: true
})
export class ParcellationDoiPipe implements PipeTransform {
public transform(_parc: SxplrParcellation): string[] {
return (_parc.li... |
d5037e6b72ca5b5e11837322a49554f485d02a556c1a2121a25a03f489e6d477 | TypeScript | 388 | 13 | import { Pipe, PipeTransform } from "@angular/core";
import { IAnnotationGeometry } from "./tools/type";
@Pipe({
name: 'annotationVisiblePipe',
pure: true
})
export class AnnotationVisiblePipe implements PipeTransform{
public transform(hiddenAnns: IAnnotationGeometry[], thisAnn: IAnnotationGeometry): boolean {
... |
912068babe8220da816c045c1880510e2e6ddeec0fe35518f54b24a3e08a1970 | TypeScript | 413 | 18 | import { Directive, HostListener } from "@angular/core";
import { MatBottomSheet } from "@angular/material/bottom-sheet";
import { ShareSheetComponent } from "./shareSheet/shareSheet.component"
@Directive({
selector: `[sxplr-share-view]`
})
export class ShareDirective{
constructor(private btmSht: MatBottomSheet)... |
cd174f3473dfcf85c268eea75be4df9242b8e4cbf65041d7a6e2f9766443bf41 | TypeScript | 416 | 6 | export { SapiViewsCoreParcellationModule } from "./module"
export { FilterGroupedParcellationPipe } from "./filterGroupedParcellations.pipe"
export { FilterUnsupportedParcPipe } from "./filterUnsupportedParc.pipe"
export { GroupedParcellation } from "./groupedParcellation"
export { ParcellationDoiPipe } from "./parcell... |
ac7dee85d4952d519a2f8560ab07d68ccd40670697eeeb3204dc1bf1b923e8b9 | TypeScript | 443 | 20 | import { Directive, HostListener } from "@angular/core";
import { TContextArg } from "src/viewerModule/viewer.interface";
import { ContextMenuService } from "./service";
@Directive({
selector: '[ctx-menu-dismiss]'
})
export class DismissCtxMenuDirective{
@HostListener('click')
onClickListener() {
this.svc.d... |
5ebff3a6d392304450f4788c2d6c2284ac18c3577802918bd2dfc4469b0acf3a | TypeScript | 451 | 15 | import { Pipe, PipeTransform } from "@angular/core";
const defaultCompareFn = (item: any, comparator: any): boolean => item === comparator
@Pipe({
name: 'includes',
})
export class IncludesPipe implements PipeTransform {
public transform(array: any[], item: any, compareFn = defaultCompareFn): boolean {
if (!... |
a91f2eda9d80b9c0ade88a69d9113ef8f2b1b75d4554e455b9aa475fa2f0216e | TypeScript | 460 | 21 | import { Injectable } from "@angular/core";
import { BehaviorSubject } from "rxjs";
@Injectable({
providedIn: 'root'
})
export class ParcellationVisibilityService {
private _visibility$ = new BehaviorSubject<boolean>(true)
public readonly visibility$ = this._visibility$.asObservable()
setVisibility(flag: boo... |
3f31ad7199320196b34a49775d1a1bffc90eb94fe7c646679a5f2d536158398a | TypeScript | 471 | 25 | import { Component, Inject } from "@angular/core";
import { MAT_DIALOG_DATA } from "@angular/material/dialog";
export type FallBackData = {
title: string
actions?: string[]
desc?: string
descMd?: string
}
@Component({
selector: 'sxplr-dialog-fallback-tmpl',
templateUrl: './tmpl.template.html',
styleUrls... |
1032e1345ff385ca53981f6a69445f22ed7abf8a9589ea4eab96f05944103cdc | TypeScript | 474 | 17 | import { Pipe, PipeTransform } from "@angular/core";
@Pipe({
name : 'regionTreeFilter',
pure: true
})
export class RegionTreeFilterPipe implements PipeTransform {
public transform<T>(array: T[], filterFn: (item: T) => boolean, getChildren: (item: T) => T[]): T[] {
const transformSingle = (item: T): boolean ... |
d01dfe6f2e0100e4d015b3ec0358bfa1f4c86dc13cd92309f624c5aef820d171 | TypeScript | 489 | 13 | const defaultCmFn = <T>(o: T, n: T) => o === n
export function arrayEqual<T>(compareFn: (o1: T, o2: T) => boolean = defaultCmFn, order = false) {
return function(array1: T[], array2: T[]){
if (order) {
for (const idx in array1) {
if (!compareFn(array1[idx], array2[idx])) return false
}
r... |
de6899110090ea9984d6f79677e658b4e23fd4b788ed6ebb70cfbd36d1bb8051 | TypeScript | 496 | 20 | import { Directive, ElementRef, Renderer2 } from "@angular/core";
@Directive({
selector : 'a[download]',
})
export class DownloadDirective {
public downloadIcon: HTMLElement
constructor(public el: ElementRef, public rd2: Renderer2) {
this.downloadIcon = rd2.createElement('i')
rd2.addClass(this.downloa... |
23483f1f70d36f021562f5df87e499a6c4fdd3b6a303cf68ad96eba92cf62c05 | TypeScript | 499 | 14 | import {Component} from "@angular/core";
import {MatDialogRef} from "@angular/material/dialog";
import { CONST } from 'common/constants'
import { PERMISSION_DIALOG_ACTIONS } from "../constrants";
@Component({
selector: 'quick-tour-start-dialog',
templateUrl: './startTourDialog.template.html',
})
export class Start... |
59e1c51a0d9a7d5de3ef99ab52c72e7ca404a322a906fd33e4d081637eb09b9c | TypeScript | 506 | 17 | import { Component } from '@angular/core'
@Component({
selector: 'iav-about',
templateUrl: './about.template.html',
styleUrls: [
'./about.style.css',
],
})
export class AboutCmp {
public supportEmailAddress: string = `support@ebrains.eu`
public contactEmailHref: string = `mailto:${this.supportEmailAdd... |
d7dbdcb7aa8e37ed83c9157ba5c54a6c0d422594f4c076743b503a6e34e29539 | TypeScript | 541 | 17 | import { Injectable } from "@angular/core";
import { Actions, createEffect, ofType } from "@ngrx/effects";
import * as atlasSelectionActions from "../atlasSelection/actions"
import * as userInterface from "../userInterface"
import { mapTo } from "rxjs/operators";
@Injectable()
export class Effect {
onStandAloneVolum... |
e80182d7ea277570109935c06d60262ca86c1fab516b3041e598169f96b7b395 | TypeScript | 541 | 22 | import { CommonModule } from "@angular/common";
import { NgModule } from "@angular/core";
import { LayoutModule } from "src/layouts/layout.module";
import { PluginModule } from "src/plugin";
import { AngularMaterialModule } from "src/sharedModules";
import { ConfigComponent } from "./configCmp/config.component";
@NgMo... |
bf2f30b140a8b75778f12aa924ef93e19cadbdb98523f1135c6e8e623a1603db | TypeScript | 553 | 23 | import { Component, TemplateRef } from "@angular/core";
import { MatDialog } from "@angular/material/dialog";
import { ARIA_LABELS } from 'common/constants'
@Component({
selector: 'sxplr-share-sheet',
templateUrl: './shareSheet.template.html',
styleUrls: [
`./shareSheet.style.css`
]
})
export class ShareS... |
8160c3648d3e28d6e50c1c561e0123b2a8db37ef00b9adedddc87c8d33bcfb9c | TypeScript | 569 | 25 | import { createSelector } from "@ngrx/store"
import { nameSpace } from "./const"
import { AtlasAppearanceStore } from "./store"
const selectStore = state => state[nameSpace] as AtlasAppearanceStore
export const octantRemoval = createSelector(
selectStore,
state => state.octantRemoval
)
export const showDelineati... |
e3a77a6d548c9b0a836257b6394fdc634d02ad5c79128034315c0c9647b125cd | TypeScript | 574 | 23 | import { CommonModule } from "@angular/common";
import { NgModule } from "@angular/core";
import { MatButtonModule } from "@angular/material/button";
import { MatTooltipModule } from "@angular/material/tooltip";
import { HideWhenLocal } from "./strictLocal.directive";
import { StrictLocalInfo } from "./strictLocalCmp/s... |
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