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Runtime error
Runtime error
chore: refactor relative calculation
Browse files
app.py
CHANGED
@@ -70,22 +70,26 @@ with col2:
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df_right.pupil_dilation = df_right.pupil_dilation.map(lambda ser: [f for f in ser if f != 0.0])
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df_right.baseline = df_right.baseline.map(lambda ser: [f for f in ser if f != 0.0])
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st.success("Blinking values have been removed!")
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if "baseline" in list(df_right.keys()):
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st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with any of the other features in a new calculated field?")
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option = st.multiselect('Select a feature to merge', [k for k in list(df_right.keys()) if k != 'baseline'], [[k for k in list(df_right.keys()) if k != 'baseline'][-2]])
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relative_key = f"relative_{option[0]}"
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add_relative = st.button(f"Add {relative_key}")
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if add_relative:
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baseline_mean = [sum(s)/len(s) for s in df['baseline']]
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df_right['relative_pupil_dilation'] = [df[option[0]][i] - baseline_mean[i] for i in range(len(df))]
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st.markdown("After adding calculated fields")
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st.dataframe(df_right)
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with open('myfile.csv') as f:
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st.download_button('Download CSV', f)
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st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
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elif detect_blinking and not number_of_blinks:
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st.caption("No blinking values were found in your data!")
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if not df_base.empty:
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st.warning("Consider running outlier detection to clean your data!", icon="⚠️")
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df_right.pupil_dilation = df_right.pupil_dilation.map(lambda ser: [f for f in ser if f != 0.0])
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df_right.baseline = df_right.baseline.map(lambda ser: [f for f in ser if f != 0.0])
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st.success("Blinking values have been removed!")
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elif detect_blinking and not number_of_blinks:
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st.caption("No blinking values were found in your data!")
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with col2:
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if 'df' in list(st.session_state.keys()):
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df_right = st.session_state.df.copy(deep=True)
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if "baseline" in list(df_right.keys()):
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st.markdown(f"A **baseline** feature has been found on your data, do you want to merge it with any of the other features in a new calculated field?")
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option = st.multiselect('Select a feature to merge', [k for k in list(df_right.keys()) if k != 'baseline'], [[k for k in list(df_right.keys()) if k != 'baseline'][-3]])
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relative_key = f"relative_{option[0]}"
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add_relative = st.button(f"Add {relative_key}")
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if add_relative:
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baseline_mean = [sum(s)/len(s) for s in df['baseline']]
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df_right['relative_pupil_dilation'] = [df[option[0]][i] - baseline_mean[i] for i in range(len(df))]
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st.markdown("After adding calculated fields")
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st.dataframe(df_right)
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with open('myfile.csv') as f:
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st.download_button('Download CSV', f)
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st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.")
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if not df_base.empty:
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st.warning("Consider running outlier detection to clean your data!", icon="⚠️")
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