polirural-attractiveness-clustering/src/adjuster/adjuster.service.js

// import attractivity from '../Attractivity.json';
import nuts from '../nuts.js';
import {factors} from './factors.js';

export class AdjusterService {
  constructor(HsCore, HsUtilsService, $rootScope, $http, $location) {
    'ngInject';
    this.HsCore = HsCore;
    this.HsUtilsService = HsUtilsService;
    this.$rootScope = $rootScope;
    this.$http = $http;
    this.serviceBaseUrl =
      $location.host() === 'localhost'
        ? 'https://jmacura.ml/ws/'
        : 'https://publish.lesprojekt.cz/nodejs/';
    this.factors = factors;
    this.clusters = [];
    this._clusteringInProcess = true;
    this.init();
  }

  /**
   * Sends a request to polirural-attractiveness-service
   * and applies the returned values
   */
  apply() {
    const f = () => {
      this._clusteringInProcess = true;
      this.$http({
        method: 'get',
        url: this.serviceBaseUrl + 'clusters',
        /*data: {
          factors: this.factors.map((f) => {
            return {
              factor: f.factor,
              weight: f.weight,
              datasets: f.datasets
                .filter((ds) => ds.included)
                .map((ds) => ds.name),
            };
          }),
        },*/
      }).then((response) => {
        const clusterData = response.data.response;
        /*let max = 0;
        this.clusters.forEach((a) => {
          if (a.aggregate > max) {
            max = a.aggregate;
          }
        });
        const normalizer = 1 / max;
        this.attractivity.forEach((a) => {
          a.aggregate *= normalizer;
        });
        this.attractivity.forEach((a) => {
          this.nutsCodeRecordRelations[a.code] = a;
        });*/
        nuts.nuts3Source.forEachFeature((feature) => {
          // Pair each feature with its clustering data
          const featureData = clusterData.find(
            (item) => item['nuts_id'] === feature.get('NUTS_ID')
          );
          Object.keys(featureData).forEach(function (key, index) {
            if (key !== 'nuts_id') {
              feature.set(key, featureData[key]);
            }
          });
        });
        const clusters = [];
        for (const region of clusterData) {
          if (!clusters.includes(region['km25.cluster'])) {
            clusters.push(region['km25.cluster']);
          }
        }
        this.clusters = clusters;
        this._clusteringInProcess = false;
        this.$rootScope.$broadcast('clusters_loaded');
      });
    };
    this.HsUtilsService.debounce(f, 300)();
  }

  init() {
    this.$http({
      url: this.serviceBaseUrl + 'datasets',
    }).then((response) => {
      this.factors = response.data.map((dataset) => {
        return {name: dataset.Factor, weight: 1, datasets: []};
      });
      this.factors = this.HsUtilsService.removeDuplicates(this.factors, 'name');
      this.factors.forEach((factor) => {
        factor.datasets = response.data
          .filter((ds) => ds.Factor === factor.name)
          .map((ds) => {
            return {
              name: ds.Name,
              included: true,
            };
          });
      });
      this.apply();
    });
  }

  /**
   * @returns {boolean} true if clustering is in process, false otherwise
   */
  isClusteringInProcess() {
    return this._clusteringInProcess;
  }
}