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added css files for cohort and individual panels

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This commit is contained in:
Carla Floricel
2022-08-03 15:59:44 -04:00
parent dac4e30a42
commit d5662113b6
928 changed files with 1102 additions and 576 deletions
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69
dashboard/flask-server/server.py
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@@ -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)