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167
apps/streamlit_data_viewer/app.py
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167
apps/streamlit_data_viewer/app.py
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import streamlit as st
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import pandas as pd
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import os
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from pathlib import Path
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import folium
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from streamlit_folium import folium_static
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import numpy as np
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st.set_page_config(page_title="Timeline CSV Viewer", layout="wide")
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st.title("Timeline CSV Viewer")
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# Path to the timeline_csv folder
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timeline_csv_path = Path("../timeline_csv")
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# Get all CSV files from all subdirectories
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csv_files = []
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if timeline_csv_path.exists():
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for subdir in timeline_csv_path.iterdir():
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if subdir.is_dir():
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for csv_file in subdir.glob("*.csv"):
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csv_files.append(csv_file)
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if not csv_files:
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st.error("No CSV files found in the timeline_csv folder.")
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st.stop()
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# Define geospatial datasets and their coordinate columns
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GEOSPATIAL_FILES = {
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'timeline_path_points.csv': {'lat': 'lat', 'lon': 'lon', 'time': 'time'},
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'visits.csv': {'lat': 'top_lat', 'lon': 'top_lon', 'time': 'startTime'},
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'raw_signals.csv': {'lat': 'lat', 'lon': 'lon', 'time': 'timestamp'},
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'frequent_places.csv': {'lat': 'lat', 'lon': 'lon', 'time': None},
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'semantic_segments.csv': {'lat': None, 'lon': None, 'time': 'startTime'}
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}
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# Create enhanced file names with geospatial indicators
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enhanced_file_names = []
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for f in csv_files:
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if f.name in GEOSPATIAL_FILES:
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enhanced_file_names.append(f"🗺️ {f.name} (Geospatial)")
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else:
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enhanced_file_names.append(f.name)
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selected_enhanced_name = st.selectbox("Select a CSV file to view:", enhanced_file_names)
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# Extract the actual filename from the enhanced name
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selected_file_name = selected_enhanced_name.replace('🗺️ ', '').replace(' (Geospatial)', '')
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# Find the full path for the selected file
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selected_file_path = None
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for file_path in csv_files:
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if file_path.name == selected_file_name:
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selected_file_path = file_path
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break
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if selected_file_path:
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st.write(f"**File:** {selected_file_path}")
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try:
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# Read the CSV file
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df = pd.read_csv(selected_file_path)
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# Display basic info
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is_geospatial = selected_file_name in GEOSPATIAL_FILES
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if is_geospatial:
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st.success(f"🗺️ **Geospatial Dataset Detected** - {df.shape[0]} rows × {df.shape[1]} columns")
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else:
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st.write(f"**Shape:** {df.shape[0]} rows × {df.shape[1]} columns")
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# Show geospatial visualization if applicable
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if is_geospatial and selected_file_name in GEOSPATIAL_FILES:
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geo_config = GEOSPATIAL_FILES[selected_file_name]
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lat_col = geo_config['lat']
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lon_col = geo_config['lon']
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time_col = geo_config['time']
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if lat_col and lon_col and lat_col in df.columns and lon_col in df.columns:
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st.subheader("🗺️ Map Visualization")
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# Filter out null coordinates
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geo_df = df.dropna(subset=[lat_col, lon_col])
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if len(geo_df) > 0:
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# Sample data if too large for performance
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if len(geo_df) > 1000:
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geo_df = geo_df.sample(n=1000)
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st.info(f"Showing 1000 randomly sampled points out of {len(df)} total points for performance")
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# Create map centered on mean coordinates
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center_lat = geo_df[lat_col].mean()
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center_lon = geo_df[lon_col].mean()
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m = folium.Map(location=[center_lat, center_lon], zoom_start=10)
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# Add points to map
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for idx, row in geo_df.iterrows():
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popup_text = f"Index: {idx}"
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if time_col and time_col in df.columns:
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popup_text += f"<br>Time: {row[time_col]}"
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# Color code based on dataset type
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if selected_file_name == 'timeline_path_points.csv':
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color = 'blue'
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elif selected_file_name == 'visits.csv':
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color = 'red'
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elif selected_file_name == 'raw_signals.csv':
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color = 'green'
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else:
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color = 'orange'
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folium.CircleMarker(
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location=[row[lat_col], row[lon_col]],
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radius=3,
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popup=popup_text,
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color=color,
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fillColor=color,
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fillOpacity=0.7
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).add_to(m)
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folium_static(m)
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# Show coordinate statistics
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st.subheader("📍 Coordinate Statistics")
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coord_stats = pd.DataFrame({
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'Statistic': ['Count', 'Min Lat', 'Max Lat', 'Min Lon', 'Max Lon', 'Center Lat', 'Center Lon'],
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'Value': [
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len(geo_df),
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f"{geo_df[lat_col].min():.6f}",
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f"{geo_df[lat_col].max():.6f}",
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f"{geo_df[lon_col].min():.6f}",
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f"{geo_df[lon_col].max():.6f}",
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f"{center_lat:.6f}",
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f"{center_lon:.6f}"
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]
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})
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st.dataframe(coord_stats)
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else:
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st.warning("No valid coordinates found in this dataset")
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else:
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if selected_file_name == 'semantic_segments.csv':
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st.info("📅 This dataset contains temporal data that links to spatial information in other datasets")
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else:
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st.warning(f"Expected coordinate columns ({lat_col}, {lon_col}) not found in this dataset")
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# Show first few rows
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st.subheader("Data Preview")
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st.dataframe(df.head(100))
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# Show column info
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st.subheader("Column Information")
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col_info = pd.DataFrame({
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'Column': df.columns,
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'Data Type': df.dtypes,
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'Non-Null Count': df.count(),
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'Null Count': df.isnull().sum()
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})
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st.dataframe(col_info)
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# Show basic statistics for numeric columns
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numeric_cols = df.select_dtypes(include=['number']).columns
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if len(numeric_cols) > 0:
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st.subheader("Numeric Column Statistics")
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st.dataframe(df[numeric_cols].describe())
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except Exception as e:
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st.error(f"Error reading the CSV file: {str(e)}")
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