StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/pxls
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readme.md

pxls unstable Build Status

Take in custom pixels container, return normalized 4-channel Uint8Array with pixels. Detects float/int input pixels, handles Infinities, unfolds structures.

npm install pxls

var pxls = require('pxls')
var ndarray = require('ndarray')

pxls([[0,0,0], [1,1,1]]) // <uint8 0,0,0,255, 255,255,255,255>
pxls([0, 1], 1) // <uint8 0,0,0,255, 255,255,255,255>
pxls([0,0,0, 1,1,1], [2,1]) // <uint8 0,0,0,255, 255,255,255,255>
pxls(new Ndarray([0,1], [2,1]) // <uint8 0,0,0,255, 255,255,255,255>
pxls(new Ndarray([0,0,0,1,1,1], [2,1,3]) // <uint8 0,0,0,255, 255,255,255,255>

let pixels = pxls(arr, shape|step?)

Takes input actual pixels container arr and returns 4 channels flat Uint8Array pixels with layout [r,g,b,a, r,g,b,a, r,g,b,a, ...].

arr can be an Array of Arrays, TypedArray, Ndarray, ImageData or DOM container Image, Video, ImageBitmap, Canvas2D, Context2D, WebGLContext.

1-channel input is mapped as grayscale [v,v,v,255, v,v,v,255]. 3-channel input fills last channel with 255 [r,g,b,255, r,g,b,255].

shape can describe the shape of input array, eg. [100,100,3] or [10,10].

step optionally can define input number of channels in flat array, that is detected automatically for nested/ndarrays.

See also

License

(c) 2018 Dmitry Yv. MIT License