conda install -c conda-forge jupyterlab
conda install -c conda-forge ipywidgets
conda install -c conda-forge cookiecutter
jupyter labextension install @jupyter-widgets/jupyterlab-manager
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{ | |
"fgm": { | |
"fragmentsServer": "//higlass.io/api/v1/", | |
"fragmentsPrecision": 2, | |
"fragmentsNoBalance": true, | |
"fragmentsDims": 60, | |
"fragmentsBaseRes": 1000, | |
"fragments": [ | |
["chrom1", "start1", "end1", "strand1", "chrom2", "start2", "end2", "strand2", "dataset", "zoomOutLevel", "cell-line"], | |
["chr1", 0, 1, "coding", "chr2", 0, 1, "coding", "CQMd6V_cRw6iCI_-Unl3PQ", 6, "GM12878"], |
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{ | |
"fgm": { | |
"fragmentsServer": "//higlass.io/api/v1/", | |
"fragmentsPrecision": 2, | |
"fragmentsNoBalance": true, | |
"fragmentsDims": 60, | |
"fragmentsBaseRes": 1000, | |
"fragments": [ | |
["chrom1", "start1", "end1", "strand1", "chrom2", "start2", "end2", "strand2", "dataset", "zoomOutLevel"], | |
["chr1", 0, 1, "coding", "chr2", 0, 1, "coding", "CQMd6V_cRw6iCI_-Unl3PQ", 6], |
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{ | |
"fgm": { | |
"fragmentsServer": "//higlass.io/api/v1/", | |
"fragmentsPrecision": 2, | |
"fragmentsDims": 22, | |
"fragmentsBaseRes": 1000, | |
"fragments": [ | |
["chrom1", "start1", "end1", "strand1", "chrom2", "start2", "end2", "strand2", "dataset", "zoomOutLevel", "size", "distance-to-diagonal", "noise", "sharpness", "_test"], | |
["chr22", 17395000, 17400000, "coding", "chr22", 17535000, 17540000, "coding", "CQMd6V_cRw6iCI_-Unl3PQ", 3, 25000000, 135000, 0.21559968240959723, 58.75532055518839, 1], | |
["chr22", 17400000, 17410000, "coding", "chr22", 17980000, 17990000, "coding", "CQMd6V_cRw6iCI_-Unl3PQ", 3, 100000000, 570000, 0.19905589933149648, 59.977490663167465, 1], |
We can make this file beautiful and searchable if this error is corrected: It looks like row 8 should actually have 17 columns, instead of 14. in line 7.
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chrom1,start1,end1,strand1,chrom2,start2,end2,strand2,dataset,zoomOutLevel,server,coords,corner-score,U-var,L-var,U-sign,L-sign | |
chr4,69285000,70075000,+,chr4,69285000,70075000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.098279,0.20712,0.43713,0.4001,0.40554 | |
chr4,18680000,20260000,+,chr4,18680000,20260000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.58392,0.31563,0.32759,0.5282,0.42433 | |
chr4,42985000,44635000,+,chr4,42985000,44635000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.71638,0.29186,0.32687,0.61341,0.53275 | |
chr4,60060000,60985000,+,chr4,60060000,60985000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.60764,0.29093,0.28665,0.41713,0.40194 | |
chr4,8640000,9145000,+,chr4,8640000,9145000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.80964,0.2152,0.33994,0.47216,0.60157 | |
chr4,22960000,24440000,+,chr4,22960000,24440000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.6919,0.25365,0.22566,0.43577,0.53228 | |
chr4,125265000,126205000,+,chr4,125265000,126205000,+,CQMd6V_cRw6iCI_-Unl3PQ,0,//higlass.io,hg19,0.96028, |
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#!/bin/bash | |
ENVIRONMENT=$1 | |
ASSEMBLY=$2 | |
RESOLUTION=$3 | |
STATES=$4 | |
STATESFN=$5 | |
MARKSFN=$6 | |
FILETOMOVESRC=$7 | |
FILETOMOVEDEST=$8 |
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import { quat, vec3 } from 'gl-matrix'; | |
import * as rbt from './rigid-body-transform.js'; | |
const myFancyRbt = rbt.create(); | |
// Rotate 45 degree around the x axis | |
const r = rbt.create({ r: quat.fromEuler(45, 0, 0) }); | |
myFancyRbt.multiply(r); | |
// Translate |
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const delayPromise = (duration) => (...args) => new Promise((resolve) => { | |
setTimeout(() => { resolve(...args); }, duration); | |
}); | |
export default delayPromise; |
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{ | |
"fgm": { | |
"fragmentsServer": "//higlass.io/api/v1/", | |
"fragmentsPrecision": 2, | |
"fragmentsDims": 60, | |
"fragmentsPadding": 50, | |
"fragmentsBaseRes": 1000, | |
"fragmentsPercentile": 99, | |
"fragmentsIgnoreDiags": 1, | |
"fragments": [ |
A somewhat random list of nice data visualization type catalogs. Mostly taken from Yuri Engelhardt's tweet
Classic: https://datavizcatalogue.com
Thorough: http://datavizproject.com