comparison src/app/services/feature-extraction/FeatureReducers.ts @ 302:c6dd5752f7f7

Change to new StreamingResponse type.

 /**
  * Created by lucast on 26/04/2017.
  */
-import {StreamingResponse} from 'piper/StreamingService';
-import {Feature, FeatureList} from 'piper/Feature';
-import {SampleType} from 'piper';
-import {
-  VectorFeatures, MatrixFeatures, TrackFeature, TrackFeatures
-} from "piper/HigherLevelUtilities";
+import {StreamingResponse} from "piper/StreamingService";
 
 export const arrayReducer = <T>(acc: T[], val: T[]): T[] => {
-  acc.push.apply(acc, val);
+  for (let i = 0, len = val.length; i < len; ++i) {
+    acc.push(val[i]);
+  }
   return acc;
 };
 
 export const typedArrayReducer = (acc: Float32Array,
                                   val: Float32Array): Float32Array => {

   acc.set(val, i);
   return acc;
 };
 
 export const streamingResponseReducer = (acc: StreamingResponse,
-                                         val: StreamingResponse,
-                                         i: number): StreamingResponse =>
-  {
-    if (acc.features.shape !== val.features.shape) {
-      throw new Error(`Unexpected feature shape ${val.features.shape} (expected ${acc.features.shape})`);
-    }
-
-    acc.processedBlockCount = val.processedBlockCount;
-
-    const isOneSamplePerStep = acc.outputDescriptor.configured.sampleType ===
-      SampleType.OneSamplePerStep;
-
-    let incoming, collected;
-
-    console.log("i = " + i + ", shape = " + acc.features.shape + ", count = " + acc.processedBlockCount);
-    
-    switch (acc.features.shape) {
-
-    case "vector":
-      incoming = val.features.collected as VectorFeatures;
-      collected = acc.features.collected as VectorFeatures;
-      if (isOneSamplePerStep) {
-	// for one sample per step vectors we know there will be
-	// totalBlockCount number of samples - so pre-allocate the
-	// Float32Array when we know the totalBlockCount (after
-	// receiving the first feature)
-	if (i === 1) {
-          const newBlock = new Float32Array(acc.totalBlockCount);
-          newBlock[0] = collected.data[0];
-          collected.data = newBlock;
-	}
-	collected.data = inPlaceTypedArrayReducer(
-	  collected.data,
-	  incoming.data,
-          i
-	);
-      } else {
-	// if not OneSamplePerStep we have to make a new array each time
-	collected.data = typedArrayReducer(
-          collected.data,
-	  incoming.data
-	);
-      }
-      break;
-
-    case "matrix":
-      incoming = val.features.collected as MatrixFeatures;
-      collected = acc.features.collected as MatrixFeatures;
-      collected.data = arrayReducer<Float32Array>(
-	collected.data,
-	incoming.data
-      );
-      break;
-
-    case "list":
-      incoming = val.features.collected as FeatureList;
-      collected = acc.features.collected as FeatureList;
-      acc.features.collected = arrayReducer<Feature>(
-	collected,
-	incoming
-      );
-      break;
-      
-    case "tracks":
-      incoming = val.features.collected as TrackFeatures;
-      collected = acc.features.collected as TrackFeatures;
-      acc.features.collected = arrayReducer<TrackFeature>(
-	collected, incoming
-      );
-      break;
-
-    default:
-      throw new Error('Invalid feature output. Aborting');
-    }
-    
-    return acc;
-  };
+                                         val: StreamingResponse):
+  StreamingResponse => {
+  acc.progress = val.progress;
+  if (val.configuration) {
+    acc.configuration = val.configuration;
+  }
+  arrayReducer(acc.features, val.features);
+  return acc;
+};