aiom_pack/src/models/transformer.js

Framework for interdependent (mcmc-like) behavioral experiments

// what kind of stimuli you wish to send to the front-end? 
// If just coordinate, use 'raw'; otherwise, you need to find out yourself.

// keep the original stimuli: use for local test
const axios = require('axios');
const fs = require('fs');
const path = require('path');
// const lower_bound = Number(process.env.lower_bound);
// const upper_bound = Number(process.env.upper_bound);

async function raw(array) {
    return array;
}

function limit_array_in_range(array, min, max) {
    return array.map((val) => {
        if (val < min) {
            const remainder = Math.abs(val-min) % (max-min);
            return max - remainder;
        }
        if (val > max) {
            const remainder = Math.abs(val-max) % (max-min);
            return min + remainder;
        }
        return val;
    });
}

// turn the stimuli into an image
function to_image(array) {
    // const rearray = process.env.experiment.includes("GSP") ? array:limit_array_in_range(array);
    const url = process.env.imageurl+'/generate';
    return axios.post(url, {
        vector: array,
    }, {headers: {
        'accept': 'application/json', 
        'Content-Type': 'application/json',
        },
        responseType: 'json',
    })
    .then(response => {
        return {
            image: `data:image/png;base64,${response.data.image}`, 
            posterior: response.data.pred_label,
        };
    })
    .catch((error) => {
        console.error('Error:', error);
    });
}

function to_image_gsp(obj) {
    const url = process.env.imageurl+'/generate_batch';
    return axios.post(url, {
        vector: obj,
    }, {headers: {
        'accept': 'application/json', 
        'Content-Type': 'application/json',
        },
        responseType: 'json',
    })
    .then(response => {
        return response.data.images.map(img => `data:image/png;base64,${img}`);
    })
    .catch((error) => {
        console.error('Error:', error);
    });
}

function grab_image(path_img) {
    // get image data from the path
    const imageData = fs.readFileSync(path_img);
    const base64 = Buffer.from(imageData).toString('base64');
    return `data:image/png;base64,${base64}`;
}

function get_attention_stimuli_path(attentionDir, current_class) {
    const dirlist = fs.readdirSync(attentionDir); 
    const matchingDirs = dirlist.filter(dir => dir.includes(current_class)); 
    if (matchingDirs.length === 0) {
        throw new Error(`No attention check directory found for class: ${current_class}`);
    }
    const attention_check_dir = matchingDirs[Math.floor(Math.random() * matchingDirs.length)];
    const s1 = attention_check_dir.split('_')[0];
    const s2 = attention_check_dir.split('_')[1];
    const example_path = path.join(attentionDir, attention_check_dir);
    // list all files in the production_example directory
    const exampleFiles = fs.readdirSync(example_path);
    const extension = exampleFiles[0].split('.').pop();
    const attention_stimulus_1 = path.join(attentionDir, attention_check_dir, s1+'.'+extension);
    const attention_stimulus_2 = path.join(attentionDir, attention_check_dir, s2+'.'+extension);
    return [attention_stimulus_1, attention_stimulus_2, [s1, s2]];
}

module.exports = {raw, to_image, to_image_gsp, grab_image, get_attention_stimuli_path, limit_array_in_range};