Update Introduction.rst

docs/BackgroundInformation/Introduction.rst

@@ -30,33 +30,38 @@ Simple Implementation:
 ===================
 For example:
 
->>> import time
->>> from pybci import PyBCI
->>> bci = PyBCI(createPseudoDevice=True)
->>> while not bci.connected:
->>>     bci.Connect()
->>>     time.sleep(1)
->>> bci.TrainMode()
->>> accuracy = 0
->>> try:
->>>     while(True):
->>>         currentMarkers = bci.ReceivedMarkerCount() # check to see how many received epochs, if markers sent to close together will be ignored till done processing
->>>         time.sleep(0.5) # wait for marker updates
->>>         print("Markers received: " + str(currentMarkers) +" Class accuracy: " + str(accuracy), end="\r")
->>>         if len(currentMarkers) > 1:  # check there is more then one marker type received
->>>             if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired:
->>>                 classInfo = bci.CurrentClassifierInfo() # hangs if called too early
->>>                 accuracy = classInfo["accuracy"]
->>>             if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired+1:  
->>>                 bci.TestMode()
->>>                 break
->>>     while True:
->>>         markerGuess = bci.CurrentClassifierMarkerGuess() # when in test mode only y_pred returned
->>>         guess = [key for key, value in currentMarkers.items() if value[0] == markerGuess]
->>>         print("Current marker estimation: " + str(guess), end="\r")
->>>         time.sleep(0.1)
->>> except KeyboardInterrupt: # allow user to break while loop
->>>     pass
+.. code-block:: python
+
+   from pybci import PyBCI
+   import time 
+   
+   if __name__ == '__main__': # Note: this line is needed when calling pseudoDevice as by default runs in a multiprocessed operation
+       bci = PyBCI(minimumEpochsRequired = 5, createPseudoDevice=True)
+       while not bci.connected: # check to see if lsl marker and datastream are available
+           bci.Connect()
+           time.sleep(1)
+       bci.TrainMode() # now both marker and datastreams available start training on received epochs
+       accuracy = 0
+       try:
+           while(True):
+               currentMarkers = bci.ReceivedMarkerCount() # check to see how many received epochs, if markers sent to close together will be ignored till done processing
+               time.sleep(0.5) # wait for marker updates
+               print("Markers received: " + str(currentMarkers) +" Accuracy: " + str(round(accuracy,2)), end="         \r")
+               if len(currentMarkers) > 1:  # check there is more then one marker type received
+                   if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired:
+                       classInfo = bci.CurrentClassifierInfo() # hangs if called too early
+                       accuracy = classInfo["accuracy"]
+                   if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired+10:  
+                       bci.TestMode()
+                       break
+           while True:
+               markerGuess = bci.CurrentClassifierMarkerGuess() # when in test mode only y_pred returned
+               guess = [key for key, value in currentMarkers.items() if value[0] == markerGuess]
+               print("Current marker estimation: " + str(guess), end="           \r")
+               time.sleep(0.2)
+       except KeyboardInterrupt: # allow user to break while loop
+           print("\nLoop interrupted by user.")
+
 
 What is PyBCI?
 ===================