Added actual tensor displaying. Added more tests. Added select channels.

examples/effects/select_channels.py

@@ -0,0 +1,9 @@
+from displayarray.effects import crop
+from displayarray import display
+import numpy as np
+
+# Scroll the mouse wheel and press ctrl, alt, or shift to select which channels are displayed as red, green, or blue.
+arr = np.ones((250, 250, 250))
+for x in range(250):
+    arr[..., x] = x / 250.0
+display(arr).block()

examples/looping/random_display.py

@@ -1,9 +1,9 @@
 from displayarray import display
 import numpy as np
 
-arr = np.random.normal(0.5, 0.1, (100, 100, 3))
+arr = np.random.normal(0.5, 0.1, (100, 100, 5))
 
 with display(arr) as displayer:
     while displayer:
-        arr[:] += np.random.normal(0.001, 0.0005, (100, 100, 3))
+        arr[:] += np.random.normal(0.001, 0.0005, (100, 100, 5))
         arr %= 1.0

examples/tensorflow/denoising_autoencoder.py

@@ -39,3 +39,9 @@ while displayer:
     autoencoder.fit(grab_noise, grab, steps_per_epoch=1, epochs=1)
     output_image = autoencoder.predict(grab, steps=1)
     displayer.update((output_image[0] * 255.0).astype(np.uint8), "uid for autoencoder output")
+
+    get_3rd_layer_output = tf.keras.backend.function([autoencoder.layers[0].input],
+                                                     [autoencoder.layers[1].output])
+    layer_output = get_3rd_layer_output([grab_noise])[0]
+
+    displayer.update(layer_output[0], "conv 1")