added css files for cohort and individual panels

dashboard/.gitignore

@@ -21,3 +21,5 @@
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
+
+/flask-server/__pycache__

dashboard/flask-server/process_input_data.py

@@ -41,6 +41,7 @@ def read_rawFacialDf(ar, id):
     facial_asym_filename = ar + "/raw_variables/"+id+"/facial/face_asymmetry/"+id+"_facasym.csv"
     facial_au_filename = ar + "/raw_variables/"+id+"/facial/face_au/"+id+"_facau.csv"
     facial_expr_filename = ar + "/raw_variables/"+id+"/facial/face_expressivity/"+id+"_facemo.csv"
+    landmark_filename = ar + "/raw_variables/"+id+"/facial/face_landmark/"+id+"_faclmk.csv"
 
     if not exists(facial_asym_filename) or not exists(facial_au_filename) or not exists(facial_expr_filename):
         return  pd.DataFrame()
@@ -51,7 +52,6 @@ def read_rawFacialDf(ar, id):
     facial_au = pd.read_csv(facial_au_filename)
     facial_au_cols = [col for col in facial_au if col not in skip_cols]
 
-
     facial_expr = pd.read_csv(facial_expr_filename)
     facial_expr_cols = [col for col in facial_expr if col not in skip_cols and "AU" not in col]
 
@@ -71,6 +71,10 @@ def read_rawMovementDf(ar, id):
     head_movement_filename = ar + "/raw_variables/"+id+"/movement/head_movement/"+id+"_headmov.csv"
     head_pose_filename = ar + "/raw_variables/"+id+"/movement/head_pose/"+id+"_headpose.csv"
 
+    blinks_filename = ar + "/raw_variables/"+id+"/movement/eye_blink/"+id+"_eyeblinks.csv"
+    fac_tremor_filename = ar + "/raw_variables/"+id+"/movement/facial_tremor/"+id+"_fac_tremor.csv"
+    voice_tremor_filename = ar + "/raw_variables/"+id+"/movement/voice_tremor/"+id+"_vtremor.csv"
+    
     if not exists(gaze_filename) or not exists(head_movement_filename) or not exists(head_pose_filename):
         return pd.DataFrame()
 
@@ -81,6 +85,16 @@ def read_rawMovementDf(ar, id):
     head_movement_cols = [col for col in head_movement if col not in skip_cols]
 
 
+    # blinks = pd.read_csv(blinks_filename)
+    # blinks_cols = [col for col in blinks if col not in skip_cols]
+
+    # fac_tremor = pd.read_csv(fac_tremor_filename)
+    # fac_tremor_cols = [col for col in fac_tremor if col not in skip_cols]
+
+    voice_tremor = pd.read_csv(voice_tremor_filename)
+    voice_tremor_cols = [col for col in voice_tremor if col not in skip_cols]
+
+
     head_pose = pd.read_csv(head_pose_filename)
     head_pose_cols = [col for col in head_pose if col not in skip_cols]
 
@@ -89,8 +103,12 @@ def read_rawMovementDf(ar, id):
         movement_df[el] = head_movement[el]
     for el in gaze_cols:
         movement_df[el] = gaze[el]
-    
-
+    # for el in blinks_cols:
+    #     movement_df[el] = blinks[el]
+    # for el in fac_tremor_cols:
+    #     movement_df[el] = fac_tremor[el]
+    for el in voice_tremor_cols:
+        movement_df[el] = voice_tremor[el]
     return movement_df.fillna(0)
 
 
@@ -104,6 +122,12 @@ def read_rawAcousticDf(ar, id):
     intt_filename = ar + "/raw_variables/"+id+"/acoustic/intensity/"+id+"_intensity.csv"
     mfcc_filename = ar + "/raw_variables/"+id+"/acoustic/mfcc/"+id+"_mfcc.csv"
     pitch_filename = ar + "/raw_variables/"+id+"/acoustic/pitch/"+id+"_pitch.csv"
+    jitter_filename = ar + "/raw_variables/"+id+"/acoustic/jitter/"+id+"_jitter.csv"
+    pause_segment_filename = ar + "/raw_variables/"+id+"/acoustic/pause_segment/"+id+"_pausechar.csv"
+    shimmer_filename = ar + "/raw_variables/"+id+"/acoustic/shimmer/"+id+"_shimmer.csv"
+    voice_frame_score_filename = ar + "/raw_variables/"+id+"/acoustic/voice_frame_score/"+id+"_voiceprev.csv"
+
+    filename_list = [fm_filename, gne_filename, hnr_filename, intt_filename,mfcc_filename, pitch_filename, pause_segment_filename, shimmer_filename, voice_frame_score_filename]
 
     if not exists(fm_filename) or not exists(gne_filename) or not exists(hnr_filename) or not exists(intt_filename) or not exists(mfcc_filename) or not exists(pitch_filename):
         return  pd.DataFrame
@@ -129,6 +153,18 @@ def read_rawAcousticDf(ar, id):
     pitch = pd.read_csv(pitch_filename)
     pitch_cols = [col for col in pitch if col not in skip_cols]
 
+    pause_segment = pd.read_csv(pause_segment_filename)
+    pause_segment_cols = [col for col in pause_segment if col not in skip_cols]
+
+    jitter = pd.read_csv(jitter_filename)
+    jitter_cols = [col for col in jitter if col not in skip_cols]
+
+    shimmer = pd.read_csv(shimmer_filename)
+    shimmer_cols = [col for col in shimmer if col not in skip_cols]
+
+    voice_frame_score = pd.read_csv(voice_frame_score_filename)
+    voice_frame_score_cols = [col for col in voice_frame_score if col not in skip_cols]
+
     acoustic_df = fm.loc[:, ~fm.columns.isin(skip_cols)].copy()
     for el in gne_cols:
         acoustic_df[el] = gne[el]
@@ -140,6 +176,14 @@ def read_rawAcousticDf(ar, id):
         acoustic_df[el] = mfcc[el]
     for el in pitch_cols:
         acoustic_df[el] = pitch[el]
+    for el in pause_segment_cols:
+        acoustic_df[el] = pause_segment[el]
+    for el in jitter_cols:
+        acoustic_df[el] = jitter[el]
+    for el in shimmer_cols:
+        acoustic_df[el] = shimmer[el]
+    for el in voice_frame_score_cols:
+        acoustic_df[el] = voice_frame_score[el]
     return acoustic_df.fillna(0)
 
 

dashboard/flask-server/server.py

@@ -111,15 +111,15 @@ def fetchIndividualFacialTimelineData():
 def getRawAttributesAndIds():
     result = {}
     if not individualFacialRawData.empty:
-        result['facial'] = [x for x in list(individualFacialRawData.columns) if x not in ["frame"]]
+        result['facial'] = [x for x in list(individualFacialRawData.columns)]
     else:
         result['facial'] =[]
     if not individualAcousticRawData.empty:
-        result['acoustic'] = [x for x in list(individualAcousticRawData.columns) if x not in ["Frames"]]
+        result['acoustic'] = [x for x in list(individualAcousticRawData.columns)]
     else:
         result['acoustic'] = []
     if not individualMovementRawData.empty:
-        result['movement'] = [x for x in list(individualMovementRawData.columns) if x not in ["Frames"]]
+        result['movement'] = [x for x in list(individualMovementRawData.columns)]
     else:
         result['movement'] = []
     if len(rawDataArgs) > 0:
@@ -151,77 +151,68 @@ def individualCorrMatrixData(id):
         all_df = f.copy()
         if len(f) == len(m):
             for x in m.columns:
-                if x not in ['Frames', 'frame']:
-                    all_df[x] = m[x]
+                all_df[x] = m[x]
         else:
             seg = int(len(m)/(max(len(f),1)))
             reminder = len(m)%(max(len(f),1))
             for i, row in all_df.iterrows():
                 for x in m.columns:
-                    if x not in ['Frames', 'frame']:
-                        if i <reminder:
-                            all_df.loc[i,x] = sum(list(m[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                        else:
-                            all_df.loc[i,x] = sum(list(m[i*seg:(i+1)*seg][x]))/seg
+                    if i <reminder:
+                        all_df.loc[i,x] = sum(list(m[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                    else:
+                        all_df.loc[i,x] = sum(list(m[i*seg:(i+1)*seg][x]))/seg
         if len(a) > len(f):
             seg = int(len(a)/(max(len(f),1)))
             reminder = len(a)%(max(len(f),1)) 
             for i, row in all_df.iterrows():
                 for x in a.columns:
-                    if x not in ['Frames', 'frame']:
-                        if i <reminder:
-                            all_df.loc[i,x] = sum(list(a[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                        else:
-                            all_df.loc[i,x] = sum(list(a[i*seg:(i+1)*seg][x]))/seg
+                    if i <reminder:
+                        all_df.loc[i,x] = sum(list(a[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                    else:
+                        all_df.loc[i,x] = sum(list(a[i*seg:(i+1)*seg][x]))/seg
         else:
             for x in a.columns:
-                if x not in ['Frames', 'frame']:
-                    all_df[x] = a[x]    
+                all_df[x] = a[x]    
     elif len(m) == min_len:
         all_df = m.copy()
         seg = int(len(f)/(max(len(m),1)))
         reminder = len(f)%(max(len(m),1))  
         for i, row in all_df.iterrows():
             for x in f.columns:
-                if x not in ['Frames', 'frame']:
-                    if i <reminder:
-                        all_df.loc[i,x] = sum(list(f[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                    else:
-                        all_df.loc[i,x] = sum(list(f[i*seg:(i+1)*seg][x]))/seg
+                if i <reminder:
+                    all_df.loc[i,x] = sum(list(f[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                else:
+                    all_df.loc[i,x] = sum(list(f[i*seg:(i+1)*seg][x]))/seg
         if len(a) > len(m):
             seg = int(len(a)/(max(len(m),1)))
             reminder = len(a)%(max(len(m),1))   
             for i, row in all_df.iterrows():
                 for x in a.columns:
-                    if x not in ['Frames', 'frame']:
-                        if i <reminder:
-                            all_df.loc[i,x] = sum(list(a[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                        else:
-                            all_df.loc[i,x] = sum(list(a[i*seg:(i+1)*seg][x]))/seg
+                    if i <reminder:
+                        all_df.loc[i,x] = sum(list(a[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                    else:
+                        all_df.loc[i,x] = sum(list(a[i*seg:(i+1)*seg][x]))/seg
         else:
             for x in a.columns:
-                if x not in ['Frames', 'frame']:
-                    all_df[x] = a[x]
+                all_df[x] = a[x]
     else:
         all_df = a.copy()
         seg = int(len(f)/(max(len(a),1)))
         reminder = len(f)%(max(len(a),1))
         for i, row in all_df.iterrows():
             for x in f.columns:
-                if x not in ['Frames', 'frame']:
-                    if i <reminder:
-                        all_df.loc[i,x] = sum(list(f[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                    else:
-                        all_df.loc[i,x] = sum(list(f[i*seg:(i+1)*seg][x]))/seg
+                if i <reminder:
+                    all_df.loc[i,x] = sum(list(f[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                else:
+                    all_df.loc[i,x] = sum(list(f[i*seg:(i+1)*seg][x]))/seg
         seg = int(len(m)/(max(len(a),1)))
         reminder = len(m)%(max(len(a),1))
         for i, row in all_df.iterrows():
             for x in m.columns:
-                if x not in ['Frames', 'frame']:
-                    if i <reminder:
-                        all_df.loc[i,x] = sum(list(m[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
-                    else:
-                        all_df.loc[i,x] = sum(list(m[i*seg:(i+1)*seg][x]))/seg
+                if i <reminder:
+                    all_df.loc[i,x] = sum(list(m[i*(seg + 1):(i+1)*(seg+1)][x]))/(seg+1)
+                else:
+                    all_df.loc[i,x] = sum(list(m[i*seg:(i+1)*seg][x]))/seg
 
     return all_df.fillna(0)