update tests

Tests/test_Pytorch.py

@@ -54,7 +54,7 @@ def PyTorchModel(x_train, x_test, y_train, y_test):
 
 #@pytest.mark.timeout(300)  # Extended timeout to 5 minutes
 def test_run_bci():
-    bci = PyBCI(minimumEpochsRequired = 3, createPseudoDevice=True, torchModel=PyTorchModel)
+    bci = PyBCI(minimumEpochsRequired = 2, createPseudoDevice=True, torchModel=PyTorchModel)
     while not bci.connected:
         bci.Connect()
         time.sleep(1)

Tests/test_Simple.py

@@ -5,12 +5,12 @@
 gs = GlobalEpochSettings()
 gs.tmax = 1 # grab 1 second after marker
 gs.tmin = 0 # grab 0 seconds before marker
-gs.splitCheck = True # splits samples between tmin and tmax
+gs.splitCheck = False # splits samples between tmin and tmax
 gs.windowLength = 0.5 # 
 gs.windowOverlap = 0.5 # windows overap by 50%, so for a total len
 
 def test_run_bci():
-    bci = PyBCI(minimumEpochsRequired = 3, createPseudoDevice=True, globalEpochSettings=gs, loggingLevel = "TIMING")
+    bci = PyBCI(minimumEpochsRequired = 2, createPseudoDevice=True, globalEpochSettings=gs, loggingLevel = "TIMING")
 
     while not bci.connected:
         bci.Connect()
@@ -32,7 +32,6 @@ def test_run_bci():
                 if accuracy > 0:
                     # set to above 0 to show some accuracy was retruend from model
                     accuracy_achieved = True
-                    bci.StopThreads()
                     bci.TestMode()
                     break
             #if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired+4:
@@ -42,6 +41,7 @@ def test_run_bci():
         guess = [key for key, value in currentMarkers.items() if value[0] == markerGuess]
         in_test_mode = True
         time.sleep(1) 
+        bci.StopThreads()
         break
         #print("Current marker estimation: " + str(guess), end="           \r")
     assert accuracy_achieved and marker_received and in_test_mode

Tests/test_Sklearn.py

@@ -5,7 +5,7 @@
 def test_run_bci():
     clf = MLPClassifier(max_iter = 1000, solver ="lbfgs")#solver=clf, alpha=alpha,hidden_layer_sizes=hid)
 
-    bci = PyBCI(minimumEpochsRequired = 3, clf = clf, createPseudoDevice=True)
+    bci = PyBCI(minimumEpochsRequired = 2, clf = clf, createPseudoDevice=True)
 
     while not bci.connected:
         bci.Connect()

Tests/test_Tensorflow.py

@@ -19,7 +19,7 @@
 
 #@pytest.mark.timeout(300)  # Extended timeout to 5 minutes
 def test_run_bci():
-    bci = PyBCI(minimumEpochsRequired = 3, model = model, createPseudoDevice=True)
+    bci = PyBCI(minimumEpochsRequired = 2, model = model, createPseudoDevice=True)
 
     while not bci.connected:
         bci.Connect()

Tests/test_zDualStream.py

@@ -11,7 +11,7 @@ def test_run_dual():
     pd2.BeginStreaming()
     time.sleep(5)
 
-    bci = PyBCI(minimumEpochsRequired = 3, createPseudoDevice=False)
+    bci = PyBCI(minimumEpochsRequired = 2, createPseudoDevice=False)
 
     while not bci.connected:
         bci.Connect()

Tests/test_zSimpleOtherFeatures.py

@@ -1,5 +1,6 @@
 from pybci import PyBCI
 from pybci.Configuration.FeatureSettings import GeneralFeatureChoices
+from pybci.Configuration.EpochSettings import IndividualEpochSetting
 from pybci.Utils.FeatureExtractor import GenericFeatureExtractor
 import time
 
@@ -21,22 +22,50 @@ def test_run_bci():
     features.zeroCross = True
     features.slopeSignChange = True
 
+    markerSettings = {}
+    markerSettings["baseline"] = IndividualEpochSetting()
+    markerSettings["baseline"].splitCheck = False
+    markerSettings["baseline"].tmin = 0      # time in seconds to capture samples before trigger
+    markerSettings["baseline"].tmax=  2      # time in seconds to capture samples after trigger
+
+    markerSettings["Marker1"] = IndividualEpochSetting()
+    markerSettings["Marker1"].splitCheck = True
+    markerSettings["Marker1"].tmin = 0      # time in seconds to capture samples before trigger
+    markerSettings["Marker1"].tmax=  2      # time in seconds to capture samples after trigger
+
     extractor = GenericFeatureExtractor(featureChoices=features)
-    bci = PyBCI(minimumEpochsRequired = 3, createPseudoDevice= True, streamCustomFeatureExtract={"PyBCIPseudoDataStream":extractor}) 
+    bci = PyBCI(minimumEpochsRequired = 2, createPseudoDevice= True, customEpochSettings=markerSettings,  streamCustomFeatureExtract={"PyBCIPseudoDataStream":extractor}) 
 
     while not bci.connected:
         bci.Connect()
         time.sleep(1)
     bci.TrainMode()
     marker_received = False
+    in_test_mode = False
+    accuracy_achieved = False
+    accuracy= 0
     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) > 2:  # check there is more then one marker type received
+        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
             marker_received = True
-            bci.StopThreads()
-            break
+            if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired:
+                classInfo = bci.CurrentClassifierInfo() # hangs if called too early
+                accuracy = classInfo["accuracy"]###
+                if accuracy > 0:
+                    # set to above 0 to show some accuracy was retruend from model
+                    accuracy_achieved = True
+                    bci.TestMode()
+                    break
             #if min([currentMarkers[key][1] for key in currentMarkers]) > bci.minimumEpochsRequired+4:
             #    break
-    assert marker_received
+    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]
+        in_test_mode = True
+        time.sleep(1) 
+        bci.StopThreads()
+        break
+        #print("Current marker estimation: " + str(guess), end="           \r")
+    assert accuracy_achieved and marker_received and in_test_mode

pybci/version.py

@@ -1,2 +1,2 @@
-__version__ = '1.4.3'
+__version__ = '1.4.4'