Committing the modifications of finding correlations between student behavior components.

.

FirstApp/api.py

@@ -1167,6 +1167,7 @@ class GetLectureActivityCorrelations(APIView):
             activity_correlations = ar.get_activity_correlations(individual_lec_activities, lec_recorded_activity_data)
 
 
+
         return Response({
             "correlations": activity_correlations
         })

FirstApp/logic/student_behavior_process.py

@@ -5,10 +5,14 @@ def calculate_student_activity_emotion_correlations(lec_activities, lec_emotions
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+    # limit = 10
+    limit = len(lec_activities)
 
     data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_activities))]
 
+    # define the correlation data dictionary
+    corr_data = {}
+
     # student gaze labels
     student_activity_labels = ['phone checking', 'listening', 'note taking']
     student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
@@ -28,29 +32,74 @@ def calculate_student_activity_emotion_correlations(lec_activities, lec_emotions
 
     # loop through the lecture activity data
     for data in lec_activities:
+        value = int(data['phone_perct'])
+        value1 = int(data['listening_perct'])
+        value2= int(data['writing_perct'])
+
+        if value != 0:
             phone_perct_list.append(int(data['phone_perct']))
+        if value1 != 0:
             listen_perct_list.append(int(data['listening_perct']))
+        if value2 != 0:
             note_perct_list.append(int(data['writing_perct']))
 
     # loop through the lecture emotion data
     for data in lec_emotions:
+        value = int(data['happy_perct'])
+        value1 = int(data['sad_perct'])
+        value2 = int(data['angry_perct'])
+        value3 = int(data['surprise_perct'])
+        value4 = int(data['neutral_perct'])
+
+        if value != 0:
             happy_perct_list.append(int(data['happy_perct']))
+        if value1 != 0:
             sad_perct_list.append(int(data['sad_perct']))
+        if value2 != 0:
             angry_perct_list.append(int(data['angry_perct']))
+        if value3 != 0:
             surprise_perct_list.append(int(data['surprise_perct']))
+        if value4 != 0:
             neutral_perct_list.append(int(data['neutral_perct']))
 
 
-    corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
-                 'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
-                 }
+    if len(phone_perct_list) == len(lec_activities):
+        corr_data[student_activity_labels[0]] = phone_perct_list
+    if len(listen_perct_list) == len(lec_activities):
+        corr_data[student_activity_labels[1]] = listen_perct_list
+    if len(note_perct_list) == len(lec_activities):
+        corr_data[student_activity_labels[2]] = note_perct_list
+    if len(happy_perct_list) == len(lec_activities):
+        corr_data[student_emotion_labels[0]] = happy_perct_list
+    if len(sad_perct_list) == len(lec_activities):
+        corr_data[student_emotion_labels[1]] = sad_perct_list
+    if len(angry_perct_list) == len(lec_activities):
+        corr_data[student_emotion_labels[2]] = angry_perct_list
+    if len(surprise_perct_list) == len(lec_activities):
+        corr_data[student_emotion_labels[3]] = surprise_perct_list
+    if len(neutral_perct_list) == len(lec_activities):
+        corr_data[student_emotion_labels[4]] = neutral_perct_list
+
+    # corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
+    #              'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
+    #              }
+
+    print('data: ', corr_data)
 
     # create the dataframe
     df = pd.DataFrame(corr_data, index=data_index)
+    df = df[(df.T != 0).any()]
+
+    print(df)
 
     # calculate the correlation
     pd_series = ut.get_top_abs_correlations(df, limit)
 
+    print(pd_series)
+
+    # assign a new value to the 'limit' variable
+    limit = len(pd_series) if len(pd_series) < limit else limit
+
     for i in range(limit):
         # this dictionary will get the pandas.Series object's  indices and values separately
         corr_dict = {}
@@ -70,6 +119,7 @@ def calculate_student_activity_emotion_correlations(lec_activities, lec_emotions
             # append the dictionary to the 'correlations' list
             correlations.append(corr_dict)
 
+
     # return the list
     return correlations
 
@@ -79,13 +129,16 @@ def calculate_student_activity_gaze_correlations(lec_activities, lec_gaze):
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+    limit = len(lec_activities)
 
     data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_activities))]
 
+    # this dictionary contains the correlation data
+    corr_data = {}
+
     # student gaze labels
     student_activity_labels = ['phone checking', 'listening', 'note taking']
-    student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
+    # student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
     student_gaze_labels = ['Up and Right', 'Up and Left', 'Down and Right', 'Down and Left', 'Front']
 
     # lecture activity data list (student)
@@ -103,30 +156,73 @@ def calculate_student_activity_gaze_correlations(lec_activities, lec_gaze):
 
     # loop through the lecture activity data
     for data in lec_activities:
+        value = int(data['phone_perct'])
+        value1 = int(data['listening_perct'])
+        value2 = int(data['writing_perct'])
+
+        if value != 0:
             phone_perct_list.append(int(data['phone_perct']))
+        if value1 != 0:
             listen_perct_list.append(int(data['listening_perct']))
+        if value2 != 0:
             note_perct_list.append(int(data['writing_perct']))
 
     # loop through the lecture activity data
     for data in lec_gaze:
+        value = int(data['looking_up_and_right_perct'])
+        value1 = int(data['looking_up_and_left_perct'])
+        value2 = int(data['looking_down_and_right_perct'])
+        value3 = int(data['looking_down_and_left_perct'])
+        value4 = int(data['looking_front_perct'])
+
+        if value != 0:
             upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        if value1 != 0:
             upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        if value2 != 0:
             downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        if value3 != 0:
             downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        if value4 != 0:
             front_perct_list.append(int(data['looking_front_perct']))
 
 
-    corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
-                 'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
-                 'Down and Left': downleft_perct_list, 'Front': front_perct_list
-                 }
+    if (len(phone_perct_list)) == len(lec_activities):
+        corr_data[student_activity_labels[0]] = phone_perct_list
+    if (len(listen_perct_list)) == len(lec_activities):
+        corr_data[student_activity_labels[1]] = listen_perct_list
+    if (len(note_perct_list)) == len(lec_activities):
+        corr_data[student_activity_labels[2]] = note_perct_list
+    if (len(upright_perct_list)) == len(lec_activities):
+        corr_data[student_gaze_labels[0]] = upright_perct_list
+    if (len(upleft_perct_list)) == len(lec_activities):
+        corr_data[student_gaze_labels[1]] = upleft_perct_list
+    if (len(downright_perct_list)) == len(lec_activities):
+        corr_data[student_gaze_labels[2]] = downright_perct_list
+    if (len(downleft_perct_list)) == len(lec_activities):
+        corr_data[student_gaze_labels[3]] = downleft_perct_list
+    if (len(front_perct_list)) == len(lec_activities):
+        corr_data[student_gaze_labels[4]] = front_perct_list
+
+    # corr_data = {'phone checking': phone_perct_list, 'listening': listen_perct_list, 'note taking': note_perct_list,
+    #              'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
+    #              'Down and Left': downleft_perct_list, 'Front': front_perct_list
+    #              }
 
     # create the dataframe
     df = pd.DataFrame(corr_data, index=data_index)
 
+    print(df)
+
     # calculate the correlation
     pd_series = ut.get_top_abs_correlations(df, limit)
 
+    print(pd_series)
+    print('length of pd_series: ', len(pd_series))
+
+    # assign a new value to the 'limit' variable
+    limit = len(pd_series) if len(pd_series) < limit else limit
+
     for i in range(limit):
         # this dictionary will get the pandas.Series object's  indices and values separately
         corr_dict = {}
@@ -146,6 +242,8 @@ def calculate_student_activity_gaze_correlations(lec_activities, lec_gaze):
             # append the dictionary to the 'correlations' list
             correlations.append(corr_dict)
 
+            print('correlations: ', correlations)
+
     # return the list
     return correlations
 
@@ -155,10 +253,13 @@ def calculate_student_emotion_gaze_correlations(lec_emotions, lec_gaze):
     # this variable will be used to store the correlations
     correlations = []
 
-    limit = 10
+    limit = len(lec_emotions)
 
     data_index = ['lecture-{}'.format(i + 1) for i in range(len(lec_emotions))]
 
+    # this dictionary will contain the correlation data
+    corr_data = {}
+
     student_emotion_labels = ['Happy', 'Sad', 'Angry', 'Surprise', 'Neutral']
     student_gaze_labels = ['Up and Right', 'Up and Left', 'Down and Right', 'Down and Left', 'Front']
 
@@ -180,32 +281,84 @@ def calculate_student_emotion_gaze_correlations(lec_emotions, lec_gaze):
 
     # loop through the lecture emotion data
     for data in lec_emotions:
+        value = int(data['happy_perct'])
+        value1 = int(data['sad_perct'])
+        value2 = int(data['angry_perct'])
+        value3 = int(data['surprise_perct'])
+        value4 = int(data['neutral_perct'])
+
+        if value != 0:
             happy_perct_list.append(int(data['happy_perct']))
+        if value1 != 0:
             sad_perct_list.append(int(data['sad_perct']))
+        if value2 != 0:
             angry_perct_list.append(int(data['angry_perct']))
+        if value3 != 0:
             surprise_perct_list.append(int(data['surprise_perct']))
+        if value4 != 0:
             neutral_perct_list.append(int(data['neutral_perct']))
 
     # loop through the lecture gaze data
     for data in lec_gaze:
+        value = int(data['looking_up_and_right_perct'])
+        value1 = int(data['looking_up_and_left_perct'])
+        value2 = int(data['looking_down_and_right_perct'])
+        value3 = int(data['looking_down_and_left_perct'])
+        value4 = int(data['looking_front_perct'])
+
+        if value != 0:
             upright_perct_list.append(int(data['looking_up_and_right_perct']))
+        if value1 != 0:
             upleft_perct_list.append(int(data['looking_up_and_left_perct']))
+        if value2 != 0:
             downright_perct_list.append(int(data['looking_down_and_right_perct']))
+        if value3 != 0:
             downleft_perct_list.append(int(data['looking_down_and_left_perct']))
+        if value4 != 0:
             front_perct_list.append(int(data['looking_front_perct']))
 
 
-    corr_data = {'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
-                 'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
-                 'Down and Left': downleft_perct_list, 'Front': front_perct_list
-                 }
+    if len(happy_perct_list) == len(lec_emotions):
+        corr_data[student_emotion_labels[0]] = happy_perct_list
+    if len(sad_perct_list) == len(lec_emotions):
+        corr_data[student_emotion_labels[1]] = sad_perct_list
+    if len(angry_perct_list) == len(lec_emotions):
+        corr_data[student_emotion_labels[2]] = angry_perct_list
+    if len(surprise_perct_list) == len(lec_emotions):
+        corr_data[student_emotion_labels[3]] = surprise_perct_list
+    if len(neutral_perct_list) == len(lec_emotions):
+        corr_data[student_emotion_labels[4]] = neutral_perct_list
+    if (len(upright_perct_list)) == len(lec_emotions):
+        corr_data[student_gaze_labels[0]] = upright_perct_list
+    if (len(upleft_perct_list)) == len(lec_emotions):
+        corr_data[student_gaze_labels[1]] = upleft_perct_list
+    if (len(downright_perct_list)) == len(lec_emotions):
+        corr_data[student_gaze_labels[2]] = downright_perct_list
+    if (len(downleft_perct_list)) == len(lec_emotions):
+        corr_data[student_gaze_labels[3]] = downleft_perct_list
+    if (len(front_perct_list)) == len(lec_emotions):
+        corr_data[student_gaze_labels[4]] = front_perct_list
+
+
+
+    # corr_data = {'Happy': happy_perct_list, 'Sad': sad_perct_list, 'Angry': angry_perct_list, 'Surprise': surprise_perct_list, 'Neutral': neutral_perct_list,
+    #              'Up and Right': upright_perct_list, 'Up and Left': upleft_perct_list, 'Down and Right': downright_perct_list,
+    #              'Down and Left': downleft_perct_list, 'Front': front_perct_list
+    #              }
 
     # create the dataframe
     df = pd.DataFrame(corr_data, index=data_index)
 
+    print(df)
+
     # calculate the correlation
     pd_series = ut.get_top_abs_correlations(df, limit)
 
+    print(pd_series)
+
+    # assign a new value to the 'limit' variable
+    limit = len(pd_series) if len(pd_series) < limit else limit
+
     for i in range(limit):
         # this dictionary will get the pandas.Series object's  indices and values separately
         corr_dict = {}

FirstApp/templates/FirstApp/Home.html

@@ -1240,7 +1240,7 @@
             function displayActivityEmotionCorrelations(correlations) {
 
                 let htmlString = "";
-
+                if (correlations.length !== 0) {
                     //create the html content for the activity correlation table
                     for (let i = 0; i < correlations.length; i++) {
                         let corr = correlations[i];
@@ -1275,6 +1275,14 @@
                         htmlString += "</tr>";
 
                     }
+                } else {
+                    htmlString += "<tr>";
+                    htmlString += "<td colspan='3'>";
+                    htmlString += "<span class='font-italic'>No correlations were found</span>";
+                    htmlString += "</td>";
+                    htmlString += "</tr>";
+                }
+
 
                 //append to the <tbody>
                 $('#student_activity_emotion_corr_tbody').append(htmlString);
@@ -1293,6 +1301,7 @@
 
                 let htmlString = "";
 
+                if (correlations.length !== 0) {
                     //create the html content for the activity correlation table
                     for (let i = 0; i < correlations.length; i++) {
                         let corr = correlations[i];
@@ -1327,6 +1336,13 @@
                         htmlString += "</tr>";
 
                     }
+                } else {
+                    htmlString += "<tr>";
+                    htmlString += "<td colspan='3'>";
+                    htmlString += "<span class='font-italic'>No correlations were found</span>";
+                    htmlString += "</td>";
+                    htmlString += "</tr>";
+                }
 
                 //append to the <tbody>
                 $('#student_activity_gaze_corr_tbody').append(htmlString);
@@ -1346,6 +1362,7 @@
 
                 let htmlString = "";
 
+                if (correlations.length !== 0) {
                     //create the html content for the activity correlation table
                     for (let i = 0; i < correlations.length; i++) {
                         let corr = correlations[i];
@@ -1380,6 +1397,14 @@
                         htmlString += "</tr>";
 
                     }
+                } else {
+                    htmlString += "<tr>";
+                    htmlString += "<td colspan='3'>";
+                    htmlString += "<span class='font-italic'>No correlations were found</span>";
+                    htmlString += "</td>";
+                    htmlString += "</tr>";
+                }
+
 
                 //append to the <tbody>
                 $('#student_emotion_gaze_corr_tbody').append(htmlString);