import {working_memory} from "@ch-bu/visualisierung-ergebnisse-studie-ward-und-kollegen"
import {studiesViz} from '@ch-bu/effekt-ward-visualisierung'
sigsquared = σ**2
abs_x = 6
area = d3.area()
.x(d => x(d.x))
.y1(d => y(d.y))
.y0(y(0))
y = d3.scaleLinear()
.domain([Math.min(d3.min(data, d => d.y),0), Math.max(1,d3.max(data, d => d.y))]).nice()
.range([innerHeight, 0])
data = {
let values = [];
for (let x = -abs_x; x < abs_x; x=x+0.01) values.push({"x":x,"y":normal_pdf(x, µ, sigsquared)});
return values;
}
function normal_pdf (input_value, mu, sigsq) {
let left_chunk = 1/(Math.sqrt(2*Math.PI*sigsq))
let right_top = -((input_value-mu)**2)
let right_bottom = 2*sigsq
return left_chunk * Math.exp(right_top/right_bottom)
}
data_null = {
let values = [];
for (let x = -abs_x; x < abs_x; x=x+0.01) values.push({"x":x,"y":normal_pdf(x, 0, sigsquared)});
return values;
}
margin = ({top: 20, left: 20, right: 20, bottom: 80})
µ = 0.31
height = 400
innerWidth = width - margin.left - margin.right
innerHeight = 400 - margin.top - margin.bottom
x = d3.scaleLinear()
.domain([-3, 6])
.range([0, innerWidth])
d3 = require('d3')
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