made some helpfile examples

release-packaging/HelpSource/Classes/FluidBufToKr.schelp

@@ -27,37 +27,51 @@ INSTANCEMETHODS::
 EXAMPLES::
 
 code::
-// make a buffer with some data in it
-~buf = Buffer.loadCollection(s,[0,1,2,3,4,7]);
 
-// play it on the server and read out of this buffer!
+// fill a 1-channel buffer with 7 numbers
+~buf = Buffer.loadCollection(s,{exprand(100,4000)} ! 7);
+
+// in a synth, read those numbers out of the buffer and get them as a control stream
 (
-{
+~synth = {
-	var sig = FluidBufToKr.kr(~buf);
+	arg buf;
+	var freqs = FluidBufToKr.kr(buf,numFrames:7);
+	var sig = SinOsc.ar(freqs.lag(0.03)) * 0.1;
 	sig.poll;
-}.play;
+	Splay.ar(sig);
+}.play(args:[\buf,~buf]);
 )
 
-// =============== passing a buffer as an argument ======================
+// then you can change what's in the buffer and it will get read out by the synth
+~buf.setn(0,{exprand(100,4000)} ! 7);
+
+::
+Use with other FluCoMa objects:
+code::
+
+// create an neural network for classification
+~mlp = FluidMLPClassifier(s);
+
+// load a model that has been pre-trained to classify between a tone and noise, simple, i know, but...
+~mlp.read(FluidFilesPath("../Resources/bufToKrExample.json"));
 
-// create a synth that both writes into a buffer (with FluidKrToBuf) and reads
+// can be used to demonstrate that...
-// out of the same buffer (with FluidBufToKr)
 (
-~synth = {
+{
-	arg buf = 999;
+	var input_buf = LocalBuf(7);
-	FluidKrToBuf.kr(SinOsc.kr(Array.fill(5,{rrand(0.0,1.0)})),buf);
+	var out_buf = LocalBuf(1);
+	var sig = Select.ar(ToggleFF.kr(Dust.kr(1)),[SinOsc.ar(440),PinkNoise.ar]);
+	var analysis = FluidSpectralShape.kr(sig);
+	FluidKrToBuf.kr(analysis,input_buf);
 
-	// you need to specify the 5 so the synth here will know how many channels to make
+	// the output prediction is written into a buffer
-	// the output proxy
+	~mlp.kr(Impulse.kr(30),input_buf,out_buf);
-	FluidBufToKr.kr(buf,numFrames:5).poll;
+
+	// and FluidBufToKr can be used to read the prediction out into a control rate stream
+	FluidBufToKr.kr(out_buf).poll;
+
+	sig.dup * -30.dbamp
 }.play;
-// you should see all zeros! (unless your buffer #999 has something in it already!)
 )
 
-// ...then after it is running, instantiate the buffer
-~buffer = Buffer.alloc(s,2);
-
-// ...then send it to the buffer
-~synth.set(\buf,~buffer);
-// you should be able to see the sine oscillators now!
 ::

release-packaging/HelpSource/Classes/FluidKrToBuf.schelp

@@ -32,25 +32,48 @@ INSTANCEMETHODS::
 
 
 EXAMPLES::
-
 code::
-(
-// FluidKrToBuf test
-s.waitForBoot{
-	Routine{
-		var buf = Buffer.alloc(s,5);
 
-		s.sync;
+(
+~synth = {
+	var buf = LocalBuf(512).clear;
+	var sig = SinOsc.ar([440,441]);
+	var lfos = Array.fill(512,{arg i; SinOsc.ar(i.linlin(0,511,0.01,0.2))});
+	FluidKrToBuf.kr(lfos,buf);
+	sig = Shaper.ar(buf,sig);
+	sig.dup * -40.dbamp;
+}.scope;
+)
 
-		{
+::
-			var sig = SinOsc.kr(rrand(1.0.dup(buf.numFrames),4.0));
+Use with other FluCoMa objects:
-			FluidKrToBuf.kr(sig,buf);
+code::
-		}.play;
 
-		1.wait;
+// make a new dataset
+~ds = FluidDataSet(s);
 
-		defer{buf.plot};
+// run a synth with varying sounds and an mfcc analysis
-	}.play;
+(
-}
+~synth = {
+	arg t_trig;
+	var buf = LocalBuf(13);
+	var n = 7;
+	var sig = BPF.ar(PinkNoise.ar.dup(n),LFDNoise1.kr(2.dup(n)).exprange(100,4000)).sum * -20.dbamp;
+	var mfccs = FluidMFCC.kr(sig,buf.numFrames,startCoeff:1,maxNumCoeffs:buf.numFrames);
+
+	// write the real-time mfcc analysis into this buffer so that...
+	FluidKrToBuf.kr(mfccs,buf);
+
+	// it can be added to the dataset from that buffer by sending a trig to the synth
+	FluidDataSetWr.kr(~ds,"point-",PulseCount.kr(t_trig),buf:buf,trig:t_trig);
+	sig.dup;
+}.play;
 )
+
+// send a bunch of triggers and...
+~synth.set(\t_trig,1);
+
+// see how your dataset grows
+~ds.print;
+
 ::

release-packaging/Resources/bufToKrExample.json

@@ -0,0 +1,107 @@
+{
+  "labels": {
+    "labels": [
+      "tone",
+      "noise"
+    ],
+    "rows": 2
+  },
+  "mlp": {
+    "layers": [
+      {
+        "activation": 1,
+        "biases": [
+          9.475189836135217e-245,
+          -7.312978302687739e-296,
+          0.0,
+          0.0,
+          0.0
+        ],
+        "cols": 5,
+        "rows": 7,
+        "weights": [
+          [
+            -0.7070957130289968,
+            0.2529529084252004,
+            -0.6315006118399392,
+            0.2641365029014226,
+            -0.11879484878940468
+          ],
+          [
+            -0.5210842572299229,
+            0.25356340881888756,
+            0.04200241621968997,
+            0.12583197506255497,
+            0.2845264672854667
+          ],
+          [
+            0.36148051326296066,
+            0.614748588906755,
+            0.24204178015383798,
+            0.6087291285844215,
+            0.5802812834026664
+          ],
+          [
+            -0.05847754423619632,
+            -0.16475294200213061,
+            -0.6962199018410693,
+            0.48955391140174614,
+            0.3708040237508281
+          ],
+          [
+            0.04633987154978883,
+            0.02745889671058346,
+            -0.16487516786286913,
+            0.03808304248049002,
+            -0.33594219806125813
+          ],
+          [
+            -0.3974517302751846,
+            0.4680556811476128,
+            -0.6125775823484346,
+            -0.5770487854150471,
+            -0.6399818221697359
+          ],
+          [
+            -0.6405756468993841,
+            -0.65821443359875,
+            -0.1166924540077695,
+            0.21767428398943345,
+            0.3338702030112145
+          ]
+        ]
+      },
+      {
+        "activation": 1,
+        "biases": [
+          -0.3628999139757816,
+          -0.42250374321781475
+        ],
+        "cols": 2,
+        "rows": 5,
+        "weights": [
+          [
+            -0.31804841180355814,
+            -0.46839296834399485
+          ],
+          [
+            -4.2805091183479895,
+            4.474227050298208
+          ],
+          [
+            0.4747799633725462,
+            0.4122869489228666
+          ],
+          [
+            0.5463246485585964,
+            0.04662010562772569
+          ],
+          [
+            3.913863778074305,
+            -3.7226634012685995
+          ]
+        ]
+      }
+    ]
+  }
+}