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| import streamlit as st | |
| import polars as pl | |
| from utils.weather_api import fetch_weather | |
| from datetime import datetime, timedelta | |
| import matplotlib.pyplot as plt | |
| def fetch_multiple_cities(start_date, end_date): | |
| 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} | |
| } | |
| city_data = {} | |
| for city, coords in locations.items(): | |
| try: | |
| df = fetch_weather(coords, start_date, end_date) | |
| city_data[city] = df.rename({"temperature_2m": city}) | |
| except Exception as e: | |
| st.error(f"Failed to fetch data for {city}: {e}") | |
| 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 display_visualizations(df): | |
| st.subheader("Daily High Temperatures Across Cities") | |
| melted_df = df.melt(id_vars=["datetime"], variable_name="City", value_name="Temperature") | |
| plt.figure(figsize=(10, 6)) | |
| for city in df.columns[1:]: | |
| plt.plot(df["datetime"], df[city], label=city) | |
| plt.xlabel("Datetime") | |
| plt.ylabel("Temperature (°F)") | |
| plt.title("Temperature Comparison") | |
| plt.legend() | |
| st.pyplot(plt) | |
| def calculate_kpis(df): | |
| kpis = {} | |
| for city in df.columns[1:]: | |
| highest = df[city].max() | |
| lowest = df[city].min() | |
| kpis[city] = (highest, lowest) | |
| return kpis | |
| def main(): | |
| st.title("Compare Weather Data for All 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()) | |
| if start_date and end_date: | |
| st.write("Fetching weather data for all BYU locations...") | |
| try: | |
| combined_data = fetch_multiple_cities(start_date, end_date) | |
| st.dataframe(combined_data) | |
| st.subheader("Key Performance Indicators (KPIs)") | |
| kpis = calculate_kpis(combined_data) | |
| for city, (highest, lowest) in kpis.items(): | |
| st.write(f"**{city}:** Highest Temp: {highest:.2f} °F | Lowest Temp: {lowest:.2f} °F") | |
| display_visualizations(combined_data) | |
| except Exception as e: | |
| st.error(f"Error: {e}") | |