Public
Edited
Mar 25, 2023
noiseFunction = d3.randomNormal(0, Math.sqrt(dt))
simulatedTrajectories = {
// Initialise an array of trajectories
const data = []
// For each trajectory…
for( let r = 0; r < numberOfTrajectories; r++){
// Determine the starting point of the run in the data array so each run is added consecutively in the array.
const startingArrayPos = r * steps
// Initialise the start of each run with the initialValue and reset the timeStep to 0
data[startingArrayPos] = {timeStep: 0, value: initialValue, run: r, time: 0}
// Step through each timeStep
for(let t = 1; t < steps; t++){
// Determine the previous value
const previousValue = data[startingArrayPos+t-1]['value']
// Calculate the new value, by applying the formula above.
const newValue = previousValue * Math.exp((mu * dt) + (sigma * noiseFunction()))
data[startingArrayPos+t] = {timeStep: t, value: newValue, run: r, time: dt * t}
}
}
return data
}
ensembleAverageOfAllTrajectories = {
const data = []
for(let t = 0; t < steps; t++ ){ // Iterate through each time step
const ensembleValue = simulatedTrajectories.filter((trajectory) => { // Select the timeStep from each run
if(trajectory.timeStep === t){
return true
}else{
return false
}
}).map((run) => { // Extract just the value at each timeStep
return run.value
}).reduce((a, b) => (a + b)) / numberOfTrajectories // Calculate the ensemble average of these values
data.push({timeStep: t, value: ensembleValue, r: "ensemble", time: t * dt})
}
return data
}
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.