Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import polars as pl | |
| from datetime import datetime, timedelta | |
| from utils.weather_api import fetch_weather | |
| locations = { | |
| "BYU Idaho": {"lat": 43.8145, "lon": -111.7833}, | |
| "BYU Hawaii": {"lat": 21.6419, "lon": -157.9267}, | |
| "BYU Provo": {"lat": 40.25, "lon": -111.65} | |
| } | |
| weather_variables = { | |
| "Temperature (°F)": "temperature_2m", | |
| "Wind Speed (mph)": "windspeed_10m", | |
| "Relative Humidity (%)": "relative_humidity_2m" | |
| } | |
| def fetch_multiple_cities(start_date, end_date, variable, timezone_selection): | |
| city_data = {} | |
| for city, coords in locations.items(): | |
| df = fetch_weather(coords, start_date, end_date, variable, timezone_selection) | |
| city_data[city] = df.rename({variable: city}) | |
| combined_df = city_data[list(city_data.keys())[0]].select("datetime") | |
| for city, data in city_data.items(): | |
| combined_df = pl.concat([combined_df, data.select(city)], how="horizontal") | |
| return combined_df | |
| def calculate_summary_metrics(df): | |
| avg_values = df.select([pl.mean(city).alias(city) for city in df.columns if city != "datetime"]) | |
| max_values = df.select([pl.max(city).alias(city) for city in df.columns if city != "datetime"]) | |
| min_values = df.select([pl.min(city).alias(city) for city in df.columns if city != "datetime"]) | |
| avg_summary = avg_values.row(0) | |
| max_summary = max_values.row(0) | |
| min_summary = min_values.row(0) | |
| return avg_summary, max_summary, min_summary | |
| def main(): | |
| st.title("Compare Weather Metrics Across BYU Locations 📊") | |
| st.sidebar.header("User Input") | |
| start_date = st.sidebar.date_input("Start Date", datetime.now() - timedelta(days=15)) | |
| end_date = st.sidebar.date_input("End Date", datetime.now()) | |
| selected_variable_label = st.sidebar.selectbox("Select Weather Variable", weather_variables.keys()) | |
| timezone_selection = st.sidebar.radio("Select Time Zone", ["America/Denver", "Pacific/Honolulu"]) | |
| selected_variable = weather_variables[selected_variable_label] | |
| if start_date and end_date: | |
| with st.spinner("Fetching weather data across all BYU locations..."): | |
| combined_data = fetch_multiple_cities(start_date, end_date, selected_variable, timezone_selection) | |
| st.write(f"### {selected_variable_label} Data Across Locations") | |
| st.dataframe(combined_data) | |
| avg_summary, max_summary, min_summary = calculate_summary_metrics(combined_data) | |
| st.subheader("Key Performance Indicators (KPIs)") | |
| kpi_cols = st.columns(len(locations)) | |
| for idx, city in enumerate(locations): | |
| with kpi_cols[idx]: | |
| st.metric(f"{city} - Avg", f"{avg_summary[idx]:.2f}") | |
| st.metric(f"{city} - Max", f"{max_summary[idx]:.2f}") | |
| st.metric(f"{city} - Min", f"{min_summary[idx]:.2f}") | |
| if __name__ == "__main__": | |
| main() | |