feat: add calculated field

app.py

@@ -17,7 +17,7 @@ with st.sidebar:
         st.text("\n".join(file_names))
 
 data_dict = dict({'trial_id':[], 'pupil_dilation':[], 'baseline':[], 'rating':[]})
-with st.spinner("building base dictionary..."):
+with st.spinner("Building base dictionary..."):
     for file_data in file_bytes:
         data = json.loads(file_data)
         for k in data:
@@ -28,7 +28,7 @@ with st.spinner("building base dictionary..."):
 df_base = pd.DataFrame()  # {'<fields>' : []})
 with col1:
     if file_bytes:
-        with st.spinner("building base dataframe..."):
+        with st.spinner("Building base dataframe..."):
             df_base = pd.DataFrame.from_dict(data_dict)
             df_base["trial_id"] = df_base.trial_id.map(lambda s: "".join([c for c in s if c.isdigit()]))
             df_base["len_pupil_dilation"] = df_base.pupil_dilation.map(lambda l: len(l))
@@ -69,14 +69,26 @@ with col2:
                 df_right = st.session_state.df.copy(deep=True)
                 df_right.pupil_dilation = df_right.pupil_dilation.map(lambda ser: [f for f in ser if f != 0.0])
                 df_right.baseline = df_right.baseline.map(lambda ser: [f for f in ser if f != 0.0])
-                st.success("blinking values have been removed!")
+                st.success("Blinking values have been removed!")
                 st.markdown("After transformation")
-                st.dataframe(df_right)
+                # st.dataframe(df_right)
+                if "baseline" in list(df_right.keys()):
+                    st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with **{[k for k in list(df_right.keys()) if k != "baseline"]}** with in a new calculated field?")
+                    relative_key = f"relative_{[k for k in list(df_right.keys()) if k != "baseline"][0]}"
+                    add_relative = st.button(f"Add {relative_key}")
+                    if add_relative:
+                        baseline_mean = [sum(s)/len(s) for s in df['baseline']]
+                        df_right['relative_pupil_dilation'] = [df['pupil_dilation'][i] - baseline_mean[i] for i in range(len(df))]
+                        st.markdown("After adding calculated fields")
+                        st.dataframe(df_right)
+                        with open('myfile.csv') as f:
+                            st.download_button('Download CSV', f)
+                        st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
         elif detect_blinking and not number_of_blinks:
-            st.caption("no blinking values were found in your data!")
+            st.caption("No blinking values were found in your data!")
 
 if not df_base.empty:
-    st.warning("consider running outlier detection to clean your data!", icon="⚠️")
+    st.warning("Consider running outlier detection to clean your data!", icon="⚠️")
 
 # for key, value in st.session_state.items():
 #    st.success(f"{key}: {value}")