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| import streamlit as st | |
| import pathlib | |
| import json | |
| import pandas as pd | |
| st. set_page_config(layout="wide") | |
| st.header("Time Series Preprocessing Pipeline") | |
| st.markdown("Users can load their time-series data and select a set of transformations to prepare a training set for univariate or multivariate time-series classification.\ | |
| Go ahead and use the sidebar on the left to upload your data files in *.json* format and start exploring and transforming it!") | |
| col1, col2 = st.columns(2) | |
| def convert_df(df): | |
| return df.to_csv(index=False).encode('utf-8') | |
| # Load a prepare data | |
| file_names, file_bytes = [], [] | |
| with st.sidebar: | |
| files = st.file_uploader("Load files", accept_multiple_files = True) | |
| if files: | |
| file_names = [file.name for file in files] | |
| file_bytes = [file.getvalue() for file in files] | |
| st.success("Your data has been successfully loaded! 🤗") | |
| data_dict = dict({'trial_id':[], 'pupil_dilation':[], 'baseline':[], 'rating':[]}) | |
| with st.spinner("Building base dictionary..."): | |
| for file_data in file_bytes: | |
| data = json.loads(file_data) | |
| for k in data: | |
| for i in data[k]: | |
| for k, v in i.items(): | |
| data_dict[k].append(v) | |
| df_base = pd.DataFrame() # {'<fields>' : []}) | |
| with col1: | |
| if file_bytes: | |
| 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)) | |
| df_base["len_baseline"] = df_base.baseline.map(lambda l: len(l)) | |
| st.info(f"number of files: {len(file_names)}") | |
| if 'df_base' not in st.session_state: | |
| st.session_state['df_base'] = df_base | |
| else: | |
| st.caption("Upload your data using the sidebar to start :sunglasses:") | |
| if 'df_base' in st.session_state: | |
| st.markdown("Your original data with some extra information about the length of the time-series fields") | |
| st.dataframe(st.session_state.df_base) | |
| # Cleaning starts | |
| with col1: | |
| if not df_base.empty: | |
| st.markdown("**Cleaning actions**") | |
| detect_blinking = st.button("I want to clean my data 🤗") | |
| number_of_blinks = 0 | |
| if detect_blinking: | |
| # Initialization of session_state | |
| if 'df' not in st.session_state: | |
| st.session_state['df'] = df_base | |
| for ser in df_base['pupil_dilation']: | |
| for f in ser: | |
| if f == 0.0: | |
| number_of_blinks += 1 | |
| for ser in df_base['baseline']: | |
| for f in ser: | |
| if f == 0.0: | |
| number_of_blinks += 1 | |
| # Initialization of session_state | |
| if 'blinks' not in st.session_state: | |
| st.session_state['blinks'] = number_of_blinks | |
| if "blinks" in st.session_state.keys(): | |
| st.info(f"blinking values (0.0) were found in {number_of_blinks} time-steps in all your data") | |
| remove_blinking = st.button("Remove blinking 🧹") | |
| # df in column 2 | |
| if remove_blinking: | |
| 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.session_state['df'] = df_right.copy(deep=True) | |
| st.success("Blinking values have been removed!") | |
| elif detect_blinking and not number_of_blinks: | |
| st.caption("No blinking values were found in your data! ") | |
| # Add calculated fields | |
| if 'df' in list(st.session_state.keys()): | |
| df_right = st.session_state.df.copy(deep=True) | |
| 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 any of the other features in a new calculated field?") | |
| option = st.multiselect('Select a feature to create relative calculated feature ➕', [k for k in list(df_right.keys()) if k != 'baseline'], [[k for k in list(df_right.keys()) if k != 'baseline'][-4]]) | |
| relative_key = f"relative_{option[0]}" | |
| add_relative = st.button(f"Add {relative_key}") | |
| if add_relative: | |
| baseline_mean = [sum(s)/len(s) for s in df_right['baseline']] | |
| df_right[relative_key] = [[field_value - baseline_mean[i] for field_value in df_right[option[0]][i]] for i in range(len(df_right))] | |
| st.markdown("After adding calculated fields") | |
| st.dataframe(df_right) | |
| csv = convert_df(df_right) | |
| # Save transformations to disk | |
| downl = st.download_button("Download CSV 💾", csv, "file.csv", "text/csv", key='download-csv') | |
| if downl: | |
| st.info("Your data has been downloaded, you can visualize and detect outliers in the 'Plotting' and 'Detect Outliers' pages on the sidebar.") | |
| if not df_base.empty: | |
| with col1: | |
| st.warning("Consider running outlier detection to clean your data!", icon="⚠️") |