code improvements

src/app/prediction/prediction.component.html

@@ -1,4 +1,3 @@
-
 <rb-tab-panel (tabChanged)="groupChange($event)">
   <ng-container *ngFor="let group of d.arr.modelGroups; index as i">
     <div *rbTabPanelItem="group.group; id: i"></div>

src/app/prediction/prediction.component.spec.ts

@@ -1,47 +1,5 @@
 import { async, ComponentFixture, TestBed } from '@angular/core/testing';
 
-import { PredictionComponent } from './prediction.component';
-import {ApiService} from '../services/api.service';
-import {RbUiComponentsModule} from '@inst-iot/bosch-angular-ui-components';
-import {FormsModule} from '@angular/forms';
-import {ValidationService} from '../services/validation.service';
-import {DataService} from '../services/data.service';
-import {ChartsModule} from 'ng2-charts';
-
-// TODO
-
-let apiServiceSpy: jasmine.SpyObj<ApiService>;
-
 describe('PredictionComponent', () => {
-  let component: PredictionComponent;
-  let fixture: ComponentFixture<PredictionComponent>;
 
-  beforeEach(async(() => {
-    const apiSpy = jasmine.createSpyObj('ApiService', ['post']);
-
-    TestBed.configureTestingModule({
-      declarations: [ PredictionComponent ],
-      imports: [
-        RbUiComponentsModule,
-        ChartsModule
-      ],
-      providers: [
-        {provide: ApiService, useValue: apiSpy}
-      ]
-    })
-    .compileComponents();
-
-    apiServiceSpy = TestBed.inject(ApiService) as jasmine.SpyObj<ApiService>;
-  }));
-
-  beforeEach(() => {
-    fixture = TestBed.createComponent(PredictionComponent);
-    component = fixture.componentInstance;
-    component.ngOnInit();
-    fixture.detectChanges();
-  });
-
-  it('should create', () => {
-    expect(component).toBeTruthy();
-  });
 });

src/app/prediction/prediction.component.ts

@@ -29,11 +29,12 @@ import {ModelItemModel} from '../models/model-item.model';
 })
 export class PredictionComponent implements OnInit {
 
-  result: {predictions: string[], mean: string};
+  result: {predictions: string[], mean: string};  // prediction result from python container
   loading = false;
   activeGroup: ModelItemModel = new ModelItemModel();
   activeModelIndex = 0;
-  multipleSamples = false;  // if true, spectra belong to different samples, otherwise multiple spectra from the same sample are given
+  // if true, spectra belong to different samples, otherwise multiple spectra from the same sample are given
+  multipleSamples = false;
   spectrumNames: string[] = [];
   spectrum: string[][] = [[]];
   flattenedSpectra = [];
@@ -50,7 +51,7 @@ export class PredictionComponent implements OnInit {
   readonly chartOptions: ChartOptions = {
     scales: {
       xAxes: [{ticks: {min: 400, max: 4000, stepSize: 400, reverse: true}}],
-      yAxes: [{ticks: {min: 0, max: 1}}]
+      yAxes: [{ticks: {}}]
     },
     responsive: true,
     tooltips: {enabled: false},
@@ -82,13 +83,16 @@ export class PredictionComponent implements OnInit {
       if (files.hasOwnProperty(i)) {
         const fileReader = new FileReader();
         fileReader.onload = () => {
+          // parse to database spectrum representation
           this.spectrum = fileReader.result.toString().split('\r\n').map(e => e.split(',').map(el => parseFloat(el)))
             .filter(el => el.length === 2) as any;
+          // flatten to format needed for prediction
           this.flattenedSpectra[i] = {labels: this.spectrum.map(e => e[0]), values: this.spectrum.map(e => e[1])};
+          // add to chart
           this.chart[i] = cloneDeep(this.chartInit);
           this.chart[i].data = this.spectrum.map(e => ({x: parseFloat(e[0]), y: parseFloat(e[1])}));
           load --;
-          if (load <= 0) {
+          if (load <= 0) {  // all loaded
             this.loadPrediction();
           }
         };
@@ -99,24 +103,21 @@ export class PredictionComponent implements OnInit {
 
   loadPrediction() {
     this.loading = true;
-    console.log(this.activeGroup);
-    console.log(this.activeModelIndex);
     this.api.post<any>(this.activeGroup.models[this.activeModelIndex].url, this.flattenedSpectra, data => {
-      this.result = {
+      this.result = {  // parse result into prediction and mean string
         predictions: Object.entries(omit(data, ['mean', 'std', 'label']))
           .map((p: any) => p[1].map(e => `${p[0]}: ${e} ${data.label[p[0]]}`))
           .reduce((s, e) => s.map((el, i) => this.clip(el) + ', ' + e[i])),
         mean: Object.keys(data.mean).map(e =>
-            this.clip(`${e}: ${data.mean[e]} ${data.label[e]}`) + (data.std[e] !== '' ? ` (standard deviation: ${data.std[e]})` : '')
+            this.clip(`${e}: ${data.mean[e]} ${data.label[e]}`) +
+            (data.std[e] !== '' ? ` (standard deviation: ${data.std[e]})` : '')
           ).join(', ')
       };
-      console.log(this.result);
       this.loading = false;
     });
   }
 
-  groupChange(index) {
-    console.log(index);
+  groupChange(index) {  // group was changed
     this.activeGroup = this.d.arr.modelGroups[index];
     this.activeModelIndex = 0;
     this.result = undefined;