Daniele Nicassio's blog

Serverize is a little tool I’ve made to help me test stuff I make. Better explained here on github.

Scores is a little app I quickly wrote to scrape musical sheets from the internet and to assemble them in a single PDF ready to be downloaded.

Behind the scenes it uses Python-driven Selenium webdrivers to do the scraping and a combination of carefully crafted hacks to make it talk with the frontend. Security was taken into (some) consideration whereas scalability was not – it only handles one request at a time for now.

Intended for (my) personal use only.

Here

 

Just a quick update, some weeks ago I rapidly coded a prototype of this idea of an estimation game, in which you have to guess different facts and the app gives an “accuracy” score. Turned out that the problem of measuring accuracy in guessing is an interesting one and could be solved in many ways. My approach was

score = min(guess,realValue)/max(guess,realValue)


To me this metric is interesting because indicates an “order of magnitude” error, that is if our guess is double or half the actual value, the score is the same.

Of course this formula is only good for positive values, and is not that good if we’re trying to guess bounded values, like if I ask to guess the year of birth of someone: you already have some very clear bounds and you will easily get a score of 95% or so.

The (very basic, only mobile) app is here:

https://estimo-app.herokuapp.com/

I didn’t bother to fix the style for desktop PC – just mobile. Here’s a screenshot:

 

 

After moving to NY, we immediately felt the need to organize some parties to meet some new people. Thus, why not designing something to make the party room a bit more “interactive” during the party?

Here’s where the party billboard comes handy: it’s a meme wall easily customizable by everyone at the party:

 

 

The top right QR code takes to the customization page in which it’s possible to customize the meme, by entering a text and choosing between a set of default images or specifying a custom publicly reachable URL to an image or GIF.

It was a cool experiment and it was fun to play with at the last party. Can’t wait for the next one!

A live version (with the limitation of 1000 simultaneous parties) is available here.

 

 

 

Some weeks ago I bought an Oculus Quest 2 and beginning experimenting with WebXR technologies. It’s real fun!

Two little projects I’ve put together are these:

1) Painting stuff in 3d: https://greeter.website/pagu-webxr/three/

You can also read the blog post on my company’s blog (in italian): https://blog.dmnk.cloud/index.php/2021/04/01/realta-virtuale-oculus-quest-2/

2) The cubes game: https://app.dmnk.cloud/the-cubes-game/three/

Also here we’ve posted to the company’s blog (still in italian): https://blog.dmnk.cloud/index.php/2021/04/19/the-cubes-game-un-piccolo-gioco-in-realta-virtuale/

While I was working on Coronamap.it, in the new section dedicated to analysis, I felt the need to do some curve fitting, that is I had a series of points and I wanted to find a curve which best fit the points, given some constraints. Of course I expected this to be a well known and well solved problem, only to discover that of course yes it is, but it’s actually something not necessarily easy to do. It’s a complex problem which requires to search in a large space of solutions, and as that not an easy task to be done in JS (which I required). Fortunately, I found an implementation in JS which used a genetic algorithm approach, so I decided to rewrite another one from scratch using my old project JSGenetic. If it does not make any sense to you, it’s because it does not have any – except that I love to make this kind of stuff.

So enough for the story, let’s talk about the library:

Example here

Usage:

var cf = GeneticCurveFitter(points, functionGenerator, 3, { //3 is the number parameters you need to tune to find the fitting curve
    RANGES: [
        [1, 2000], //one range for every parameter - if not specified, they fall back to [-1000,1000] which is kind of silly
        [1580000000000, 1590000000000],
        [10000000, 1000000000]
    ]
});

functionGenerator

is a function which gets the generated parameters in input and returns the function which you want to optimize. Easier to explain with an example:

//polynomial functionGenerator
function polyGen(coeffs) {
  return function(x) {
    var result = 0;
    for(var i=0; i<coeffs.length; i++) {
      result += coeffs[i]*Math.pow(x,i);
    }
    return result;
  }
}

//gaussian from the example
var gaussGen = function (coeff) {
    return function (x) {
        return coeff[0] * Math.exp(-((x - coeff[1]) * (x - coeff[1])) / (2 * coeff[2] * coeff[2]));
    }
}

After initializing the object, it can be easily run like this:

var resultFn =   cf.fit();

which returns the best fit function it could find. The process takes some seconds and the time can vary by CPU power and number of coefficients to find. The returned function also has an helper method to generate a chart from it (read: to sample it) which goes like this:

var samples = resultFn.toDataset(startX,endX,numberOfSamples);
//samples is in the form [{x:,y:}, ...]

There are also other methods to better control the evolution of the coefficients: here are the library’s returned methods:

{
        step: step, //make a single step of the genetic algoritm, or N steps if you call cf.step(N)
        fit: fit, //already described before
        getCurrentSolution: getCurrentSolution, //gets current solution
        getCurrentFitness: getCurrentFitness,//gets current error
        getCurrentCoefficients: getCurrentCoefficients //get raw coefficients as an array
}

That’s all! Enjoy.